Aic: aikake information criterian Bic: basian

links: https://learningstatisticswithr.com/book/regression.html http://www.learnbymarketing.com/tutorials/linear-regression-in-r/ https://data.princeton.edu/R/linearModels http://www.clayford.net/statistics/using-natural-splines-in-linear-modeling/ https://stackoverflow.com/questions/24192428/what-does-the-capital-letter-i-in-r-linear-regression-formula-mean https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-019-0666-3 https://rdrr.io/cran/AICcmodavg/man/AICcmodavg-package.html

Research Question
Goal of this analysis is to answer one of my four main research questions:
-Is variation in salinity, air temperature, or pH associated with changes in abundance or reproductive effort of Fucus distichus populations in SFE?
Note: I still don’t have the air temperature data completed so I’ll be focusing on salinity and pH. I do have water temeprature so I may look at that instead of air temp for now.

Data
Field data is the mean of these values per survey. Environmental data is the median of hourly median data inbetween field survey dates.

I will be using the combined environmental and field data at all sites. Right now I have the following match-ups for field site and water data source: China Camp and Paradise Cay, EOS and Point Chauncy, Richardson Bay and Brickyard Park, and Fort Point and Horseshoe Bay. I could match Paradise Cay with EOS instead of China Camp but that’s something I need to look into more.

Data for abundace: no.fuc.q (density: total number of fucus per quadrat), cover (percent conver fucus per quadrat), no.large.fuc.q (number of large fucus per quadrat), and no.small.fuc.q (number of small fucus per quadrat)

Data for reproductive effort: covcl.repro (cover class of reproductive tissue), dw.repro (dry weight of reproductive tissue), apices.repro (number of reproductive apices), perc.ra (percent of apices that are reproductive), avg.oog (average number of oogonia) and perc.rdw (percent of dry weight that is reproductive tissue)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Breaking up main research question
Given these variables, I’m going to break up my research question into discrete subquestions that I can run individual tests for:
Q1. Effects of salinity and pH on abundance

Q2. Effects of salinity and pH on reproductive effort

Question for Karina:
-I’m not sure how to do every combination of all these variables? Should I see which ones have any significance and then try combos of those? Should I look at terms individually and then look at interactions with significate terms? –>because I’m not sure I didn’t look at a lot of interaction terms until I clarify this. For now the only interaction term I have is salinity:ph to keep it simple -Not sure if I should be looking at the lm or anova results for significance

Notes
Linear model google doc from Karina:

Plot interpretation

Reporting

Initial results summary:
Significate = P<0.05
Weak/slight = P<0.1
If no effect, not listed below. All variables tested are listed under “Data” section above
Not sure if I should be looking at the lm or anova results for these values. The difference between them is usually small but does make some values significant vs weakly significant

Q1. Effects of salinity and pH on abundance

Q2. Effects of salinity and pH on reproductive effort

Initial interpretation of results
(these are just initial impressions and not well articulated/thought out yet) -Salintiy has more of an impact on Fucus abundance while pH has more of an impact on fucus reproduction –>does this align with the results I see in my experiment? -Salinity impacts density but not cover, suggesting that cover is maintained even as density composition (size of thalli) changes. The composition changes but the cover remains. This is seen in my field work as well, density declined but cover remained pretty constant. -Density of small thalli are effected by changes to salinity but large thalli are not suggesting small thalli are driving the driving factor to why density is effected but not cover since larger thalli contribute more to cover than small thalli. Again,this pattern is also seen in my field work; total density pattern follows the small thalli density pattern more than large thalli. -Since small thalli are affected by salinity and large are not: This suggests that there’s some critical size that once fucus reaches it’s more tolerant to salinity changes –> suggestion for future studies -Cover class of reproductive tissue (percent cover that is reproductive tissue) is affected by salinity and I wonder if this trend is also mainly driven by the amount of small thalli? Since small thalli tend to have less reproductive tissue than large thalli -Need to look at the direction of the relationship of ph and repro (linear equation)

Set up

rm(list=ls())

library(tidyverse)
library(ggpubr)
library(scales)
library(chron)
library(plotly)
library(taRifx)
library(aweek)
library(easypackages)
library(renv)
library(here)
library(ggthemes)
library(gridExtra)
library(patchwork)
library(tidyquant)
library(recipes) 
library(cranlogs)
library(knitr)
library(openair)

Read in data

#read in data
all<-read.csv(
      "https://raw.githubusercontent.com/Cmwegener/thesis/master/data/environment_field/envi.field.all.csv",
    header = TRUE
  )

####Linear Model –> not best fit####
####Q1. Effects of salinity and pH on abundance####
Redundant from above, just placing it here for reference

Data for abundace: no.fuc.q (density: total number of fucus per quadrat), cover (percent conver fucus per quadrat), no.large.fuc.q (number of large fucus per quadrat), and no.small.fuc.q (number of small fucus per quadrat)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

####Q1.1 Effect of salinity and pH on total density#### Different salinity terms first

Effect of pH and salinity on density check median field values to see if it fits model better

lm1 <- lm(no.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##   -184.5269       5.1369      21.8195      -0.3502
summary (lm1)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.766 -24.732  -9.569  12.324 114.619 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -184.5269  1341.0810  -0.138    0.891
## salinity       5.1369    59.4977   0.086    0.932
## ph            21.8195   169.5065   0.129    0.898
## salinity:ph   -0.3502     7.5249  -0.047    0.963
## 
## Residual standard error: 35.05 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2028, Adjusted R-squared:  0.1445 
## F-statistic: 3.478 on 3 and 41 DF,  p-value: 0.02437
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  12663 12663.2 10.3086 0.002575 **
## ph           1    150   149.8  0.1219 0.728743   
## salinity:ph  1      3     2.7  0.0022 0.963106   
## Residuals   41  50365  1228.4                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

See if poission with glm improves fit Anova table: Summary table: -Intercept, salinity estimate the sal slope -12.429 + pH axis –> a negative plane 3D -sal, ph, neg effect on density looking at estimate std. coefficients (w/o interaction term) -Residuals with min, 1q ect: distance between line and data point (thinking of classic lm graph) –> similar to boxplot distribution of data Effect of pH and salinity on density, interaction term removed

lm2 <- lm(no.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -123.937        2.368       14.160
summary (lm2)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.557 -25.080  -9.866  12.772 114.596 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -123.937    317.924  -0.390  0.69863   
## salinity       2.368      0.725   3.266  0.00217 **
## ph            14.160     40.067   0.353  0.72556   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.63 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2028, Adjusted R-squared:  0.1648 
## F-statistic: 5.342 on 2 and 42 DF,  p-value: 0.008569
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  12663 12663.2 10.5595 0.002279 **
## ph         1    150   149.8  0.1249 0.725556   
## Residuals 42  50367  1199.2                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density: daily minimum salinity

lm3 <- lm(no.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -144.448          2.473         17.432
summary (lm3)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -45.624 -22.021  -8.684  13.224 109.527 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -144.4482   320.5812  -0.451  0.65479    
## daily.min.sal    2.4728     0.6856   3.607  0.00087 ***
## ph              17.4321    40.4589   0.431  0.66894    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.37 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2504, Adjusted R-squared:  0.212 
## F-statistic: 6.515 on 2 and 39 DF,  p-value: 0.003621
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.min.sal  1  15174 15174.4 12.8440 0.0009288 ***
## ph             1    219   219.3  0.1856 0.6689416    
## Residuals     39  46076  1181.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density: daily maximum salinity

lm4 <- lm(no.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##       -266.06           3.09          28.90
summary (lm4)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.199 -25.713  -8.414  10.070 113.527 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -266.062    346.094  -0.769  0.44667   
## daily.max.sal    3.090      1.075   2.875  0.00651 **
## ph              28.897     43.053   0.671  0.50605   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.06 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1752, Adjusted R-squared:  0.1329 
## F-statistic: 4.143 on 2 and 39 DF,  p-value: 0.02337
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  10185 10185.0  7.8348 0.007927 **
## ph             1    586   585.6  0.4505 0.506055   
## Residuals     39  50699  1300.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density: daily salinity range

lm5 <- lm(no.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##          99.341           -3.002           -4.698
summary (lm5)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40.17 -24.69 -11.75  13.54 112.23 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       99.341    344.177   0.289   0.7744  
## daily.sal.range   -3.002      1.217  -2.468   0.0181 *
## ph                -4.698     43.534  -0.108   0.9146  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.91 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1354, Adjusted R-squared:  0.09111 
## F-statistic: 3.055 on 2 and 39 DF,  p-value: 0.05854
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1   8310  8310.1  6.0984 0.01801 *
## ph               1     16    15.9  0.0116 0.91461  
## Residuals       39  53144  1362.7                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density: daily median salinity

lm6 <- lm(no.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -196.058          2.501         23.198
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.647 -24.381  -9.045  11.477 112.508 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -196.0575   328.8472  -0.596   0.5545   
## daily.med.sal    2.5014     0.7464   3.351   0.0018 **
## ph              23.1979    41.3616   0.561   0.5781   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.97 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2239, Adjusted R-squared:  0.1841 
## F-statistic: 5.626 on 2 and 39 DF,  p-value: 0.007135
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  13378 13378.4 10.9368 0.002032 **
## ph             1    385   384.8  0.3146 0.578103   
## Residuals     39  47706  1223.2                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          14.10374            0.03033            3.73310
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.779 -24.751 -14.509   5.582 119.874 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        14.10374  383.28117   0.037    0.971
## min.daily.sal.lt5   0.03033    0.46452   0.065    0.948
## ph                  3.73310   48.82415   0.076    0.939
## 
## Residual standard error: 38.78 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0003993,  Adjusted R-squared:  -0.0472 
## F-statistic: 0.008389 on 2 and 42 DF,  p-value: 0.9916
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1     16   16.44  0.0109 0.9172
## ph                 1      9    8.79  0.0058 0.9394
## Residuals         42  63155 1503.69
plot (lm7)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          -152.9242             -0.4275             25.7050
summary (lm8)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34.99 -25.97 -13.55  10.87 113.78 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -152.9242   392.5235  -0.390    0.699
## min.daily.sal.lt10   -0.4275     0.4826  -0.886    0.381
## ph                   25.7050    50.1266   0.513    0.611
## 
## Residual standard error: 38.42 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01863,    Adjusted R-squared:  -0.0281 
## F-statistic: 0.3986 on 2 and 42 DF,  p-value: 0.6738
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1    789  788.74  0.5343 0.4689
## ph                  1    388  388.21  0.2630 0.6108
## Residuals          42  62003 1476.27
plot (lm8)

Effect of salinity and pH on density: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##          -153.9049             -0.5798             26.2061
summary (lm9)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37.86 -24.15 -12.53  13.53 110.96 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -153.9049   367.1179  -0.419    0.677
## min.daily.sal.lt15   -0.5798     0.4456  -1.301    0.200
## ph                   26.2061    46.8088   0.560    0.579
## 
## Residual standard error: 38.02 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03904,    Adjusted R-squared:  -0.006717 
## F-statistic: 0.8532 on 2 and 42 DF,  p-value: 0.4333
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   2014 2013.63  1.3930 0.2445
## ph                  1    453  453.09  0.3134 0.5786
## Residuals          42  60714 1445.56
plot (lm9)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          -7.66066           -0.03285            6.58464
summary (lm10)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.908 -25.195 -14.608   6.303 119.175 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -7.66066  394.09790  -0.019    0.985
## max.daily.sal.lt5  -0.03285    0.47589  -0.069    0.945
## ph                  6.58464   50.25393   0.131    0.896
## 
## Residual standard error: 38.78 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0004113,  Adjusted R-squared:  -0.04719 
## F-statistic: 0.00864 on 2 and 42 DF,  p-value: 0.9914
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1      0    0.17  0.0001 0.9916
## ph                 1     26   25.82  0.0172 0.8964
## Residuals         42  63154 1503.68
plot (lm10)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          -20.07430            -0.06723             8.21161
summary (lm11)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -36.988 -25.527 -14.688   6.664 118.770 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -20.07430  393.83075  -0.051    0.960
## max.daily.sal.lt10  -0.06723    0.47862  -0.140    0.889
## ph                   8.21161   50.22743   0.163    0.871
## 
## Residual standard error: 38.77 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0007673,  Adjusted R-squared:  -0.04682 
## F-statistic: 0.01613 on 2 and 42 DF,  p-value: 0.984
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1      8    8.30  0.0055 0.9411
## ph                  1     40   40.18  0.0267 0.8709
## Residuals          42  63132 1503.14
plot (lm11)

Effect of salinity and pH on density: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##            -68.795              -0.195              14.600
summary (lm12)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.330 -25.875 -14.445   7.855 117.150 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -68.7946   397.3691  -0.173    0.863
## max.daily.sal.lt15  -0.1950     0.4881  -0.400    0.692
## ph                  14.5999    50.7172   0.288    0.775
## 
## Residual standard error: 38.71 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.004083,   Adjusted R-squared:  -0.04334 
## F-statistic: 0.08609 on 2 and 42 DF,  p-value: 0.9177
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1    134  133.81  0.0893 0.7665
## ph                  1    124  124.15  0.0829 0.7749
## Residuals          42  62922 1498.15
plot (lm12)

Effect of salinity and pH on density: number of days with a daily salinity range greater than 10

lm13 <- lm(no.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             -76.8805               -0.2548               15.7331
summary (lm13)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.851 -26.997 -12.829   8.551 116.344 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -76.8805   384.9463  -0.200    0.843
## daily.sal.range.gt10  -0.2548     0.4875  -0.523    0.604
## ph                    15.7331    49.1061   0.320    0.750
## 
## Residual standard error: 38.65 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.00676,    Adjusted R-squared:  -0.04054 
## F-statistic: 0.1429 on 2 and 42 DF,  p-value: 0.8672
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1    274  273.71  0.1832 0.6708
## ph                    1    153  153.37  0.1026 0.7503
## Residuals            42  62753 1494.13
plot (lm13)

Effect of salinity and pH on density: number of days with a daily salinity range greater than 5

lm14 <- lm(no.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            -85.4916              -0.6222              17.7824
summary (lm14)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.50 -26.10 -11.83  12.96 110.38 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -85.4916   350.6630  -0.244    0.809
## daily.sal.range.gt5  -0.6222     0.4350  -1.430    0.160
## ph                   17.7824    44.6095   0.399    0.692
## 
## Residual standard error: 37.87 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04674,    Adjusted R-squared:  0.001348 
## F-statistic:  1.03 on 2 and 42 DF,  p-value: 0.366
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1   2725 2725.29  1.9005 0.1753
## ph                   1    228  227.86  0.1589 0.6922
## Residuals           42  60227 1433.98
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on density: daily minimum ph

lm3 <- lm(no.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     -111.157        12.719         2.365
summary (lm3)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.605 -25.064  -9.764  11.464 114.542 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -111.157    377.136  -0.295  0.76964   
## daily.min.ph   12.719     48.204   0.264  0.79318   
## salinity        2.365      0.726   3.258  0.00222 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.65 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2018, Adjusted R-squared:  0.1637 
## F-statistic: 5.308 on 2 and 42 DF,  p-value: 0.008808
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1      0     0.0   0.000 0.995557   
## salinity      1  12747 12746.8  10.615 0.002225 **
## Residuals    42  50434  1200.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density: daily maximum ph

lm4 <- lm(no.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -291.107        34.632         2.441
summary (lm4)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.55 -24.21 -11.86  12.16 100.84 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -291.1068   216.4112  -1.345   0.1858   
## daily.max.ph   34.6323    26.7412   1.295   0.2024   
## salinity        2.4412     0.7137   3.420   0.0014 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.01 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2311, Adjusted R-squared:  0.1945 
## F-statistic: 6.313 on 2 and 42 DF,  p-value: 0.004007
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.ph  1   1073  1072.8  0.9275 0.341025   
## salinity      1  13530 13530.4 11.6984 0.001404 **
## Residuals    42  48577  1156.6                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density: daily ph range

lm5 <- lm(no.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -11.450          -4.285           2.436
summary (lm5)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42.17 -20.97  -7.65   9.22 115.01 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    -11.4496    17.5761  -0.651  0.51832   
## daily.ph.range  -4.2853     3.3174  -1.292  0.20350   
## salinity         2.4359     0.7134   3.414  0.00143 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.01 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.231,  Adjusted R-squared:  0.1944 
## F-statistic: 6.308 on 2 and 42 DF,  p-value: 0.004024
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.ph.range  1   1107  1107.1   0.957 0.333550   
## salinity        1  13487 13486.5  11.658 0.001428 **
## Residuals      42  48587  1156.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density: daily median ph

lm6 <- lm(no.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -239.636        28.767         2.381
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.271 -24.516  -8.035  14.396 111.682 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -239.6360   309.1487  -0.775  0.44259   
## daily.med.ph   28.7666    38.9615   0.738  0.46442   
## salinity        2.3814     0.7209   3.303  0.00196 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.46 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2107, Adjusted R-squared:  0.1731 
## F-statistic: 5.605 on 2 and 42 DF,  p-value: 0.006956
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1    353   353.3  0.2976 0.588299   
## salinity      1  12957 12957.2 10.9124 0.001958 **
## Residuals    42  49870  1187.4                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density: number of days with a daily minimun ph less than 7

lm7 <- lm(no.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          -9.3193           -0.2842            2.2829
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.62 -24.19 -10.48  12.34 114.32 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -9.3193    20.3782  -0.457  0.64980   
## min.daily.ph.lt7  -0.2842     1.1325  -0.251  0.80309   
## salinity           2.2829     0.7719   2.958  0.00507 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.66 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2016, Adjusted R-squared:  0.1636 
## F-statistic: 5.303 on 2 and 42 DF,  p-value: 0.008838
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt7  1   2234  2233.8   1.860 0.179893   
## salinity          1  10505 10505.0   8.747 0.005073 **
## Residuals        42  50442  1201.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density: number of days with a daily minimun ph less than 8

lm7 <- lm(no.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##       -11.950715          0.008093          2.350374
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.293 -24.408  -9.666  10.205 114.751 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -11.950715  19.158498  -0.624  0.53614   
## min.daily.ph.lt8   0.008093   0.287905   0.028  0.97771   
## salinity           2.350374   0.724336   3.245  0.00231 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.68 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2004, Adjusted R-squared:  0.1624 
## F-statistic: 5.265 on 2 and 42 DF,  p-value: 0.009116
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1      0     0.0   0.000 0.997176   
## salinity          1  12664 12664.1  10.529 0.002309 **
## Residuals        42  50516  1202.8                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density: number of days with a daily maximum ph less than 7

lm10 <- lm(no.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          -9.3193           -0.2842            2.2829
summary (lm10)
## 
## Call:
## lm(formula = no.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39.62 -24.19 -10.48  12.34 114.32 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -9.3193    20.3782  -0.457  0.64980   
## max.daily.ph.lt7  -0.2842     1.1325  -0.251  0.80309   
## salinity           2.2829     0.7719   2.958  0.00507 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.66 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2016, Adjusted R-squared:  0.1636 
## F-statistic: 5.303 on 2 and 42 DF,  p-value: 0.008838
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.ph.lt7  1   2234  2233.8   1.860 0.179893   
## salinity          1  10505 10505.0   8.747 0.005073 **
## Residuals        42  50442  1201.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             -20.4275                0.7835                2.5404
summary (lm13)
## 
## Call:
## lm(formula = no.fuc.q ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -37.264 -23.539  -9.462  15.100 105.976 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          -20.4275    20.2601  -1.008  0.31910   
## daily.ph.range.gt0.5   0.7835     0.8823   0.888  0.37957   
## salinity               2.5404     0.7489   3.392  0.00152 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34.36 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.2152, Adjusted R-squared:  0.1778 
## F-statistic: 5.757 on 2 and 42 DF,  p-value: 0.006171
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                      Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.ph.range.gt0.5  1      9     8.7  0.0073 0.932104   
## salinity              1  13586 13585.6 11.5072 0.001522 **
## Residuals            42  49586  1180.6                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q1.2 Effect of salinity and pH on percent cover#### Different salinity terms first

Effect of pH and salinity on percent cover

lm1 <- lm(cover ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = cover ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##   -110.4421       0.9746      17.8257      -0.1015
summary (lm1)
## 
## Call:
## lm(formula = cover ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.901 -10.707   2.686   9.201  25.669 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -110.4421   576.9700  -0.191    0.849
## salinity       0.9746    25.5976   0.038    0.970
## ph            17.8257    72.9264   0.244    0.808
## salinity:ph   -0.1015     3.2374  -0.031    0.975
## 
## Residual standard error: 15.08 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02461,    Adjusted R-squared:  -0.04676 
## F-statistic: 0.3448 on 3 and 41 DF,  p-value: 0.7931
anova (lm1)
## Analysis of Variance Table
## 
## Response: cover
##             Df Sum Sq Mean Sq F value Pr(>F)
## salinity     1   53.0  53.036  0.2333 0.6317
## ph           1  181.9 181.905  0.8000 0.3763
## salinity:ph  1    0.2   0.224  0.0010 0.9751
## Residuals   41 9322.3 227.373
plot (lm1)

Effect of pH and salinity on percent cover, interaction term removed

lm2 <- lm(cover ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = cover ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -92.8748       0.1718      15.6049
summary (lm2)
## 
## Call:
## lm(formula = cover ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.985 -10.714   2.776   9.240  25.666 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -92.8748   136.7776  -0.679    0.501
## salinity      0.1718     0.3119   0.551    0.585
## ph           15.6049    17.2377   0.905    0.370
## 
## Residual standard error: 14.9 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02458,    Adjusted R-squared:  -0.02187 
## F-statistic: 0.5292 on 2 and 42 DF,  p-value: 0.5929
anova (lm2)
## Analysis of Variance Table
## 
## Response: cover
##           Df Sum Sq Mean Sq F value Pr(>F)
## salinity   1   53.0  53.036  0.2389 0.6275
## ph         1  181.9 181.905  0.8195 0.3705
## Residuals 42 9322.5 221.965
plot (lm2)

Effect and salinity and pH on percent cover: daily minimum salinity

lm3 <- lm(cover ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = cover ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     -115.6212         0.3202        18.2198
summary (lm3)
## 
## Call:
## lm(formula = cover ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.768 -10.390   1.473   9.378  23.411 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -115.6212   132.9446  -0.870    0.390
## daily.min.sal    0.3202     0.2843   1.126    0.267
## ph              18.2198    16.7782   1.086    0.284
## 
## Residual standard error: 14.25 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05499,    Adjusted R-squared:  0.006532 
## F-statistic: 1.135 on 2 and 39 DF,  p-value: 0.3319
anova (lm3)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.min.sal  1  221.5  221.54  1.0904 0.3028
## ph             1  239.6  239.59  1.1792 0.2842
## Residuals     39 7923.9  203.18
plot (lm3)

Effect and salinity and pH on percent cover: daily maximum salinity

lm4 <- lm(cover ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = cover ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -88.1379        -0.1069        15.9476
summary (lm4)
## 
## Call:
## lm(formula = cover ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.867  -8.030   2.629   7.618  27.056 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -88.1379   138.9217  -0.634    0.529
## daily.max.sal  -0.1069     0.4314  -0.248    0.806
## ph             15.9476    17.2813   0.923    0.362
## 
## Residual standard error: 14.47 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0258, Adjusted R-squared:  -0.02416 
## F-statistic: 0.5165 on 2 and 39 DF,  p-value: 0.6007
anova (lm4)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1   38.0  37.979  0.1813 0.6726
## ph             1  178.4 178.371  0.8516 0.3618
## Residuals     39 8168.7 209.453
plot (lm4)

Effect and salinity and pH on percent cover: daily salinity range

lm5 <- lm(cover ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = cover ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         -63.850           -0.984           13.233
summary (lm5)
## 
## Call:
## lm(formula = cover ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.074  -5.018   2.491   7.784  22.061 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     -63.8495   127.4745  -0.501   0.6193  
## daily.sal.range  -0.9840     0.4506  -2.184   0.0351 *
## ph               13.2333    16.1239   0.821   0.4168  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.67 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1306, Adjusted R-squared:  0.08599 
## F-statistic: 2.929 on 2 and 39 DF,  p-value: 0.06531
anova (lm5)
## Analysis of Variance Table
## 
## Response: cover
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  969.0  969.01  5.1839 0.02836 *
## ph               1  125.9  125.91  0.6736 0.41679  
## Residuals       39 7290.1  186.93                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent cover: daily median salinity

lm6 <- lm(cover ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = cover ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##     -111.7802         0.1878        18.0310
summary (lm6)
## 
## Call:
## lm(formula = cover ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.104  -9.359   2.210   8.239  24.938 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   -111.7802   135.5369  -0.825    0.415
## daily.med.sal    0.1878     0.3076   0.611    0.545
## ph              18.0310    17.0475   1.058    0.297
## 
## Residual standard error: 14.42 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03351,    Adjusted R-squared:  -0.01606 
## F-statistic: 0.676 on 2 and 39 DF,  p-value: 0.5145
anova (lm6)
## Analysis of Variance Table
## 
## Response: cover
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1   48.5  48.481  0.2333 0.6318
## ph             1  232.5 232.464  1.1187 0.2967
## Residuals     39 8104.1 207.797
plot (lm6)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 5

lm7 <- lm(cover ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          -36.0949             0.1483             8.7112
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.411 -11.846   2.987   8.651  25.328 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -36.0949   146.5782  -0.246    0.807
## min.daily.sal.lt5   0.1483     0.1776   0.835    0.409
## ph                  8.7112    18.6718   0.467    0.643
## 
## Residual standard error: 14.83 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03357,    Adjusted R-squared:  -0.01245 
## F-statistic: 0.7295 on 2 and 42 DF,  p-value: 0.4882
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1  273.0 272.988  1.2413 0.2716
## ph                 1   47.9  47.868  0.2177 0.6432
## Residuals         42 9236.6 219.919
plot (lm7)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 10

lm8 <- lm(cover ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           -29.5696              0.1467              7.8372
summary (lm8)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.972 -11.594   2.727   8.979  25.709 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -29.5696   151.6388  -0.195    0.846
## min.daily.sal.lt10   0.1467     0.1864   0.787    0.436
## ph                   7.8372    19.3648   0.405    0.688
## 
## Residual standard error: 14.84 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03181,    Adjusted R-squared:  -0.0143 
## F-statistic: 0.6899 on 2 and 42 DF,  p-value: 0.5072
anova (lm8)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1  267.9 267.895  1.2159 0.2764
## ph                  1   36.1  36.087  0.1638 0.6877
## Residuals          42 9253.5 220.321
plot (lm8)

Effect of salinity and pH on percent cover: number of days with a daily minimun salinity less than 15

lm9 <- lm(cover ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           -53.4032              0.1105             10.9020
summary (lm9)
## 
## Call:
## lm(formula = cover ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.009 -11.290   3.372   8.821  26.016 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -53.4032   143.6906  -0.372    0.712
## min.daily.sal.lt15   0.1105     0.1744   0.633    0.530
## ph                  10.9020    18.3210   0.595    0.555
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02683,    Adjusted R-squared:  -0.01951 
## F-statistic: 0.5789 on 2 and 42 DF,  p-value: 0.5649
anova (lm9)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1  178.0 178.006  0.8038 0.3751
## ph                  1   78.4  78.414  0.3541 0.5550
## Residuals          42 9301.1 221.454
plot (lm9)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 5

lm10 <- lm(cover ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##         -53.27195            0.08252           10.97750
summary (lm10)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.704 -11.961   3.464   8.314  25.987 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       -53.27195  151.59490  -0.351    0.727
## max.daily.sal.lt5   0.08252    0.18306   0.451    0.654
## ph                 10.97750   19.33083   0.568    0.573
## 
## Residual standard error: 14.92 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02226,    Adjusted R-squared:  -0.0243 
## F-statistic: 0.4782 on 2 and 42 DF,  p-value: 0.6232
anova (lm10)
## Analysis of Variance Table
## 
## Response: cover
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1  141.0  141.03  0.6339 0.4304
## ph                 1   71.7   71.75  0.3225 0.5731
## Residuals         42 9344.7  222.49
plot (lm10)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 10

lm11 <- lm(cover ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          -50.86686             0.08983            10.65914
summary (lm11)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.864 -11.980   3.443   8.346  25.914 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -50.86686  151.45641  -0.336    0.739
## max.daily.sal.lt10   0.08983    0.18406   0.488    0.628
## ph                  10.65914   19.31608   0.552    0.584
## 
## Residual standard error: 14.91 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02307,    Adjusted R-squared:  -0.02345 
## F-statistic: 0.496 on 2 and 42 DF,  p-value: 0.6125
anova (lm11)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1  152.8 152.823  0.6874 0.4117
## ph                  1   67.7  67.696  0.3045 0.5840
## Residuals          42 9337.0 222.308
plot (lm11)

Effect of salinity and pH on percent cover: number of days with a daily maximum salinity less than 15

lm12 <- lm(cover ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           -39.3514              0.1178              9.1462
summary (lm12)
## 
## Call:
## lm(formula = cover ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.462 -12.122   3.162   8.527  25.652 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -39.3514   152.7899  -0.258    0.798
## max.daily.sal.lt15   0.1178     0.1877   0.628    0.534
## ph                   9.1462    19.5010   0.469    0.641
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02666,    Adjusted R-squared:  -0.01969 
## F-statistic: 0.5752 on 2 and 42 DF,  p-value: 0.5669
anova (lm12)
## Analysis of Variance Table
## 
## Response: cover
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1  206.1 206.102  0.9305 0.3402
## ph                  1   48.7  48.722  0.2200 0.6415
## Residuals          42 9302.6 221.492
plot (lm12)

Effect of salinity and pH on percent cover: number of days with a daily salinity range greater than 10

lm13 <- lm(cover ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             -45.6667                0.1188                9.9397
summary (lm13)
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.453 -11.799   3.159   8.621  25.673 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -45.6667   148.2006  -0.308    0.759
## daily.sal.range.gt10   0.1188     0.1877   0.633    0.530
## ph                     9.9397    18.9054   0.526    0.602
## 
## Residual standard error: 14.88 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.02682,    Adjusted R-squared:  -0.01952 
## F-statistic: 0.5787 on 2 and 42 DF,  p-value: 0.565
anova (lm13)
## Analysis of Variance Table
## 
## Response: cover
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1  195.1 195.107  0.8810 0.3533
## ph                    1   61.2  61.216  0.2764 0.6018
## Residuals            42 9301.1 221.456
plot (lm13)

Effect of salinity and pH on percent cover: number of days with a daily salinity range greater than 5

lm14 <- lm(cover ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##           -85.53608             -0.01366             15.22127
summary (lm14)
## 
## Call:
## lm(formula = cover ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.370 -10.375   3.372   8.326  27.130 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -85.53608  138.44943  -0.618    0.540
## daily.sal.range.gt5  -0.01366    0.17174  -0.080    0.937
## ph                   15.22127   17.61282   0.864    0.392
## 
## Residual standard error: 14.95 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01768,    Adjusted R-squared:  -0.0291 
## F-statistic: 0.378 on 2 and 42 DF,  p-value: 0.6875
anova (lm14)
## Analysis of Variance Table
## 
## Response: cover
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1    2.0   2.034  0.0091 0.9245
## ph                   1  167.0 166.951  0.7469 0.3924
## Residuals           42 9388.5 223.535
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent cover: daily minimum ph

lm3 <- lm(cover ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = cover ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##    -226.4095       32.9061        0.1914
summary (lm3)
## 
## Call:
## lm(formula = cover ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.152  -8.640   2.190   8.433  26.218 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -226.4095   158.8270  -1.426    0.161
## daily.min.ph   32.9061    20.3006   1.621    0.113
## salinity        0.1914     0.3057   0.626    0.535
## 
## Residual standard error: 14.59 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.0641, Adjusted R-squared:  0.01953 
## F-statistic: 1.438 on 2 and 42 DF,  p-value: 0.2488
anova (lm3)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1  529.2  529.17  2.4847 0.1225
## salinity      1   83.4   83.44  0.3918 0.5347
## Residuals    42 8944.9  212.97
plot (lm3)

Effect salinity and pH on percent cover: daily maximum ph

lm4 <- lm(cover ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = cover ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##    -101.1884       16.3572        0.1951
summary (lm4)
## 
## Call:
## lm(formula = cover ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.792 -10.444   2.664   9.191  25.666 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -101.1884    93.5205  -1.082    0.285
## daily.max.ph   16.3572    11.5560   1.415    0.164
## salinity        0.1951     0.3084   0.632    0.531
## 
## Residual standard error: 14.7 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.05083,    Adjusted R-squared:  0.00563 
## F-statistic: 1.125 on 2 and 42 DF,  p-value: 0.3344
anova (lm4)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1  399.4  399.39  1.8491 0.1811
## salinity      1   86.4   86.40  0.4000 0.5305
## Residuals    42 9071.7  215.99
plot (lm4)

Effect of salinity and pH on percent cover: daily ph range

lm5 <- lm(cover ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = cover ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        30.9302         -2.4813          0.2018
summary (lm5)
## 
## Call:
## lm(formula = cover ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.605  -7.993   1.361  10.018  24.691 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     30.9302     7.5043   4.122 0.000173 ***
## daily.ph.range  -2.4813     1.4164  -1.752 0.087094 .  
## salinity         0.2018     0.3046   0.662 0.511364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.52 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.07327,    Adjusted R-squared:  0.02914 
## F-statistic:  1.66 on 2 and 42 DF,  p-value: 0.2023
anova (lm5)
## Analysis of Variance Table
## 
## Response: cover
##                Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range  1  607.7  607.74  2.8819 0.09698 .
## salinity        1   92.5   92.52  0.4387 0.51136  
## Residuals      42 8857.2  210.89                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on percent cover: daily median ph

lm6 <- lm(cover ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = cover ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##    -126.5690       19.8597        0.1736
summary (lm6)
## 
## Call:
## lm(formula = cover ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.383  -9.539   2.907   8.866  25.690 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -126.5690   132.7535  -0.953    0.346
## daily.med.ph   19.8597    16.7307   1.187    0.242
## salinity        0.1736     0.3096   0.561    0.578
## 
## Residual standard error: 14.8 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.03783,    Adjusted R-squared:  -0.00799 
## F-statistic: 0.8256 on 2 and 42 DF,  p-value: 0.4449
anova (lm6)
## Analysis of Variance Table
## 
## Response: cover
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1  292.6 292.645  1.3366 0.2542
## salinity      1   68.9  68.896  0.3147 0.5778
## Residuals    42 9195.9 218.951
plot (lm6)

Effect salinity and pH on percent cover: number of days with a daily minimun ph less than 7

lm7 <- lm(cover ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         30.87373          -0.01436           0.14869
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.089 -10.410   2.387  10.749  25.942 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      30.87373    8.84568   3.490  0.00115 **
## min.daily.ph.lt7 -0.01436    0.49161  -0.029  0.97683   
## salinity          0.14869    0.33505   0.444  0.65948   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.04 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.005569,   Adjusted R-squared:  -0.04178 
## F-statistic: 0.1176 on 2 and 42 DF,  p-value: 0.8893
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1    8.7   8.662  0.0383 0.8458
## salinity          1   44.6  44.567  0.1969 0.6595
## Residuals        42 9504.2 226.291
plot (lm7)

Effect salinity and pH on percent cover: number of days with a daily minimun ph less than 8

lm7 <- lm(cover ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          26.1581            0.1951            0.1569
summary (lm7)
## 
## Call:
## lm(formula = cover ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -31.609  -8.827   4.655   9.818  24.726 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       26.1581     8.0651   3.243  0.00232 **
## min.daily.ph.lt8   0.1951     0.1212   1.610  0.11493   
## salinity           0.1569     0.3049   0.515  0.60959   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.6 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.06334,    Adjusted R-squared:  0.01874 
## F-statistic:  1.42 on 2 and 42 DF,  p-value: 0.253
anova (lm7)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1  549.0  548.97  2.5756 0.1160
## salinity          1   56.4   56.42  0.2647 0.6096
## Residuals        42 8952.1  213.14
plot (lm7)

Effect salinity and pH on percent cover: number of days with a daily maximum ph less than 7

lm10 <- lm(cover ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = cover ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         30.87373          -0.01436           0.14869
summary (lm10)
## 
## Call:
## lm(formula = cover ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.089 -10.410   2.387  10.749  25.942 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      30.87373    8.84568   3.490  0.00115 **
## max.daily.ph.lt7 -0.01436    0.49161  -0.029  0.97683   
## salinity          0.14869    0.33505   0.444  0.65948   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.04 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.005569,   Adjusted R-squared:  -0.04178 
## F-statistic: 0.1176 on 2 and 42 DF,  p-value: 0.8893
anova (lm10)
## Analysis of Variance Table
## 
## Response: cover
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1    8.7   8.662  0.0383 0.8458
## salinity          1   44.6  44.567  0.1969 0.6595
## Residuals        42 9504.2 226.291
plot (lm10)

Effect salinity and pH on percent cover: number of days with a daily ph range greater than 0.5

lm13 <- lm(cover ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = cover ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              29.4557                0.1170                0.1805
summary (lm13)
## 
## Call:
## lm(formula = cover ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.186 -10.680   1.816  11.369  26.223 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)           29.4557     8.8603   3.324  0.00185 **
## daily.ph.range.gt0.5   0.1170     0.3859   0.303  0.76314   
## salinity               0.1805     0.3275   0.551  0.58445   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.03 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.007723,   Adjusted R-squared:  -0.03953 
## F-statistic: 0.1634 on 2 and 42 DF,  p-value: 0.8498
anova (lm13)
## Analysis of Variance Table
## 
## Response: cover
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1    5.2   5.220  0.0231 0.8799
## salinity              1   68.6  68.591  0.3038 0.5845
## Residuals            42 9483.7 225.801
plot (lm13)

####Q1.3 Effect of salinity and pH on density of large thalli#### Different salinity terms first

Effect of pH and salinity on density of small thalli

lm1 <- lm(no.small.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##      99.394       -9.315      -15.294        1.496
summary (lm1)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.967 -23.684  -7.834  11.576 105.023 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   99.394   1355.640   0.073    0.942
## salinity      -9.315     61.497  -0.151    0.880
## ph           -15.294    171.383  -0.089    0.929
## salinity:ph    1.496      7.782   0.192    0.849
## 
## Residual standard error: 34.06 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2411, Adjusted R-squared:  0.1812 
## F-statistic: 4.024 on 3 and 38 DF,  p-value: 0.014
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  13787 13787.0 11.8856 0.001397 **
## ph           1    172   172.2  0.1484 0.702198   
## salinity:ph  1     43    42.9  0.0370 0.848559   
## Residuals   38  44079  1160.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on density of small thalli, interaction term removed

lm2 <- lm(no.small.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -152.793        2.508       16.598
summary (lm2)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.326 -23.908  -5.857  10.830 105.180 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -152.7933   338.2143  -0.452  0.65394   
## salinity       2.5082     0.7141   3.513  0.00114 **
## ph            16.5983    42.5489   0.390  0.69859   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.64 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2403, Adjusted R-squared:  0.2014 
## F-statistic: 6.169 on 2 and 39 DF,  p-value: 0.0047
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  13787 13787.0 12.1865 0.001212 **
## ph         1    172   172.2  0.1522 0.698585   
## Residuals 39  44122  1131.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density of small thalli: daily minimum salinity

lm3 <- lm(no.small.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -183.477          2.608         21.225
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.490 -21.038  -6.786  13.881  99.876 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -183.4772   340.9496  -0.538 0.593795    
## daily.min.sal    2.6080     0.6751   3.863 0.000449 ***
## ph              21.2245    42.9501   0.494 0.624190    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.25 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.293,  Adjusted R-squared:  0.2538 
## F-statistic: 7.461 on 2 and 36 DF,  p-value: 0.001946
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.min.sal  1  16230 16230.4 14.6784 0.0004918 ***
## ph             1    270   270.0  0.2442 0.6241902    
## Residuals     36  39806  1105.7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density of small thalli: daily maximum salinity

lm4 <- lm(no.small.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -306.065          3.304         32.422
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.241 -27.190  -6.240   9.158 104.202 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -306.065    372.207  -0.822  0.41632   
## daily.max.sal    3.304      1.068   3.094  0.00381 **
## ph              32.422     46.226   0.701  0.48758   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35.15 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2101, Adjusted R-squared:  0.1662 
## F-statistic: 4.786 on 2 and 36 DF,  p-value: 0.01435
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  11219 11219.5  9.0806 0.004709 **
## ph             1    608   607.8  0.4919 0.487579   
## Residuals     36  44479  1235.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density of small thalli: daily salinity range

lm5 <- lm(no.small.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         117.659           -3.064           -7.809
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.022 -25.359  -9.762  18.193 103.312 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      117.659    369.866   0.318   0.7522  
## daily.sal.range   -3.064      1.213  -2.526   0.0161 *
## ph                -7.809     46.805  -0.167   0.8684  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.45 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1505, Adjusted R-squared:  0.1033 
## F-statistic:  3.19 on 2 and 36 DF,  p-value: 0.05303
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                 Df Sum Sq Mean Sq F value Pr(>F)  
## daily.sal.range  1   8440  8439.5  6.3521 0.0163 *
## ph               1     37    37.0  0.0278 0.8684  
## Residuals       36  47830  1328.6                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density of small thalli: daily median salinity

lm6 <- lm(no.small.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -235.012          2.653         26.894
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.445 -22.694  -6.716  10.877 102.999 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -235.0118   351.1338  -0.669  0.50758    
## daily.med.sal    2.6530     0.7377   3.597  0.00096 ***
## ph              26.8938    44.0903   0.610  0.54571    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.92 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2643, Adjusted R-squared:  0.2235 
## F-statistic: 6.468 on 2 and 36 DF,  p-value: 0.003983
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  14456 14455.6 12.5632 0.001112 **
## ph             1    428   428.1  0.3721 0.545712   
## Residuals     36  41423  1150.6                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.small.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##           67.3390             0.1074            -3.9749
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.640 -24.536 -19.983   9.136 112.055 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        67.3390   407.1119   0.165    0.869
## min.daily.sal.lt5   0.1074     0.4664   0.230    0.819
## ph                 -3.9749    51.7961  -0.077    0.939
## 
## Residual standard error: 38.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.001359,   Adjusted R-squared:  -0.04985 
## F-statistic: 0.02653 on 2 and 39 DF,  p-value: 0.9738
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1     70   70.15  0.0472 0.8292
## ph                 1      9    8.76  0.0059 0.9392
## Residuals         39  58002 1487.23
plot (lm7)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.small.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##            -99.199              -0.385              17.979
summary (lm8)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31.80 -25.43 -20.08  15.77 105.65 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -99.1986   416.3303  -0.238    0.813
## min.daily.sal.lt10  -0.3850     0.4857  -0.793    0.433
## ph                  17.9788    53.0959   0.339    0.737
## 
## Residual standard error: 38.28 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01586,    Adjusted R-squared:  -0.03461 
## F-statistic: 0.3142 on 2 and 39 DF,  p-value: 0.7322
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1    753  753.01  0.5138 0.4778
## ph                  1    168  168.05  0.1147 0.7367
## Residuals          39  57160 1465.64
plot (lm8)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.small.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           -94.2296             -0.5522             17.7609
summary (lm9)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34.41 -25.91 -19.30  18.34 102.57 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -94.2296   390.5885  -0.241    0.811
## min.daily.sal.lt15  -0.5522     0.4506  -1.225    0.228
## ph                  17.7609    49.7215   0.357    0.723
## 
## Residual standard error: 37.87 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03707,    Adjusted R-squared:  -0.01231 
## F-statistic: 0.7508 on 2 and 39 DF,  p-value: 0.4787
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   1970 1970.27  1.3739 0.2482
## ph                  1    183  182.98  0.1276 0.7229
## Residuals          39  55928 1434.05
plot (lm9)

Effect of salinity and pH on percent no.small.fuc.q: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.small.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          60.18350            0.07026           -3.01930
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.805 -24.494 -20.035   9.795 111.699 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        60.18350  418.22060   0.144    0.886
## max.daily.sal.lt5   0.07026    0.47745   0.147    0.884
## ph                 -3.01930   53.26598  -0.057    0.955
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0005555,  Adjusted R-squared:  -0.0507 
## F-statistic: 0.01084 on 2 and 39 DF,  p-value: 0.9892
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1     27   27.48  0.0185 0.8926
## ph                 1      5    4.78  0.0032 0.9551
## Residuals         39  58049 1488.43
plot (lm10)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.small.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           44.71116             0.02656            -0.99326
summary (lm11)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33.89 -24.64 -19.91  10.43 111.21 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         44.71116  417.96578   0.107    0.915
## max.daily.sal.lt10   0.02656    0.48049   0.055    0.956
## ph                  -0.99326   53.24028  -0.019    0.985
## 
## Residual standard error: 38.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  7.888e-05,  Adjusted R-squared:  -0.0512 
## F-statistic: 0.001538 on 2 and 39 DF,  p-value: 0.9985
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1      4    4.06  0.0027 0.9586
## ph                  1      1    0.52  0.0003 0.9852
## Residuals          39  58076 1489.14
plot (lm11)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.small.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##            -9.3238             -0.1244              6.0992
summary (lm12)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.20 -25.88 -18.84  12.47 109.36 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -9.3238   421.6289  -0.022    0.982
## max.daily.sal.lt15  -0.1244     0.4906  -0.254    0.801
## ph                   6.0992    53.7459   0.113    0.910
## 
## Residual standard error: 38.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.001646,   Adjusted R-squared:  -0.04955 
## F-statistic: 0.03216 on 2 and 39 DF,  p-value: 0.9684
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1     76   76.47  0.0514 0.8218
## ph                  1     19   19.15  0.0129 0.9102
## Residuals          39  57985 1486.80
plot (lm12)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 10

lm13 <- lm(no.small.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##              -14.573                -0.171                 6.849
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34.94 -25.87 -19.11  13.15 108.73 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -14.5733   408.7095  -0.036    0.972
## daily.sal.range.gt10  -0.1710     0.4915  -0.348    0.730
## ph                     6.8493    52.0640   0.132    0.896
## 
## Residual standard error: 38.53 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.003094,   Adjusted R-squared:  -0.04803 
## F-statistic: 0.06051 on 2 and 39 DF,  p-value: 0.9414
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1    154  153.99  0.1037 0.7491
## ph                    1     26   25.69  0.0173 0.8960
## Residuals            39  57901 1484.65
plot (lm13)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 5

lm14 <- lm(no.small.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            -47.2864              -0.5375              11.8989
summary (lm14)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -32.04 -26.93 -17.67  17.34 103.00 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -47.2864   382.4803  -0.124    0.902
## daily.sal.range.gt5  -0.5375     0.4471  -1.202    0.237
## ph                   11.8989    48.6295   0.245    0.808
## 
## Residual standard error: 37.9 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03573,    Adjusted R-squared:  -0.01372 
## F-statistic: 0.7226 on 2 and 39 DF,  p-value: 0.4919
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1   1989 1989.44  1.3854 0.2463
## ph                   1     86   85.98  0.0599 0.8080
## Residuals           39  56006 1436.04
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent density of small thalli: daily minimum ph

lm3 <- lm(no.small.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     -13.6056       -0.9499        2.4765
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.194 -24.868  -6.745  11.281 105.344 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -13.6056   370.3267  -0.037  0.97088   
## daily.min.ph  -0.9499    47.3143  -0.020  0.98408   
## salinity       2.4765     0.7135   3.471  0.00128 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.7 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2374, Adjusted R-squared:  0.1983 
## F-statistic:  6.07 on 2 and 39 DF,  p-value: 0.00507
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1    105   105.1  0.0926 0.762573   
## salinity      1  13682 13682.3 12.0471 0.001282 **
## Residuals    39  44294  1135.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density of small thalli: daily maximum ph

lm4 <- lm(no.small.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -341.122        39.658         2.602
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.381 -23.622  -7.539  11.878  89.092 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -341.1220   217.0709  -1.571 0.124151    
## daily.max.ph   39.6584    26.8116   1.479 0.147134    
## salinity        2.6019     0.6971   3.733 0.000604 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.79 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2779, Adjusted R-squared:  0.2409 
## F-statistic: 7.504 on 2 and 39 DF,  p-value: 0.001749
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.max.ph  1   1156  1156.3  1.0752 0.3061523    
## salinity      1  14984 14983.5 13.9328 0.0006035 ***
## Residuals    39  41941  1075.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density of small thalli: daily ph range

lm5 <- lm(no.small.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -20.804          -3.688           2.556
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -41.62 -22.73  -3.88  10.88 105.48 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -20.804     17.221  -1.208 0.234280    
## daily.ph.range   -3.688      3.239  -1.139 0.261816    
## salinity          2.556      0.703   3.636 0.000799 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.15 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2619, Adjusted R-squared:  0.2241 
## F-statistic:  6.92 on 2 and 39 DF,  p-value: 0.00268
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range  1    679   679.0  0.6177 0.4366425    
## salinity        1  14533 14533.0 13.2214 0.0007989 ***
## Residuals      39  42869  1099.2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density of small thalli: daily median ph

lm6 <- lm(no.small.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -274.633        31.976         2.527
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.495 -23.717  -7.765  11.459 101.857 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -274.6333   311.9059  -0.881 0.383982    
## daily.med.ph   31.9756    39.2661   0.814 0.420400    
## salinity        2.5270     0.7078   3.570 0.000965 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.42 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2501, Adjusted R-squared:  0.2117 
## F-statistic: 6.504 on 2 and 39 DF,  p-value: 0.00365
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.med.ph  1    291   291.1  0.2607 0.6125424    
## salinity      1  14236 14236.4 12.7480 0.0009653 ***
## Residuals    39  43553  1116.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 7

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         -17.9249           -0.3602            2.3933
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.431 -25.043  -7.569  12.120 104.758 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -17.9249    19.9066  -0.900  0.37341   
## min.daily.ph.lt7  -0.3602     1.1042  -0.326  0.74603   
## salinity           2.3933     0.7559   3.166  0.00299 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.65 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2394, Adjusted R-squared:  0.2004 
## F-statistic: 6.139 on 2 and 39 DF,  p-value: 0.004808
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt7  1   2552  2551.9   2.253 0.141407   
## salinity          1  11356 11355.6  10.026 0.002994 **
## Residuals        39  44174  1132.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 8

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##        -21.63396           0.02606           2.47772
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.627 -24.759  -6.982  11.226 105.359 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -21.63396   18.70568  -1.157  0.25449   
## min.daily.ph.lt8   0.02606    0.28599   0.091  0.92786   
## salinity           2.47772    0.71106   3.485  0.00123 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.7 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2375, Adjusted R-squared:  0.1984 
## F-statistic: 6.075 on 2 and 39 DF,  p-value: 0.00505
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1      9     9.2  0.0081 0.928714   
## salinity          1  13787 13787.2 12.1419 0.001234 **
## Residuals        39  44285  1135.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily maximum ph less than 7

lm10 <- lm(no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         -17.9249           -0.3602            2.3933
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.431 -25.043  -7.569  12.120 104.758 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -17.9249    19.9066  -0.900  0.37341   
## max.daily.ph.lt7  -0.3602     1.1042  -0.326  0.74603   
## salinity           2.3933     0.7559   3.166  0.00299 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.65 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2394, Adjusted R-squared:  0.2004 
## F-statistic: 6.139 on 2 and 39 DF,  p-value: 0.004808
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.ph.lt7  1   2552  2551.9   2.253 0.141407   
## salinity          1  11356 11355.6  10.026 0.002994 **
## Residuals        39  44174  1132.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density of small thalli: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.small.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             -32.0043                0.9839                2.7283
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.198 -22.623  -9.266  14.220  94.110 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -32.0043    19.8146  -1.615 0.114333    
## daily.ph.range.gt0.5   0.9839     0.8777   1.121 0.269188    
## salinity               2.7283     0.7348   3.713 0.000639 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.17 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2612, Adjusted R-squared:  0.2233 
## F-statistic: 6.893 on 2 and 39 DF,  p-value: 0.002732
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range.gt0.5  1      0     0.1  0.0001 0.9927205    
## salinity              1  15169 15169.3 13.7865 0.0006391 ***
## Residuals            39  42912  1100.3                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q1.4 Effect of salinity and pH on desnity of small thalli#### Different salinity terms first

Effect of pH and salinity on density of small thalli

lm1 <- lm(no.small.fuc.q ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##      99.394       -9.315      -15.294        1.496
summary (lm1)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.967 -23.684  -7.834  11.576 105.023 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   99.394   1355.640   0.073    0.942
## salinity      -9.315     61.497  -0.151    0.880
## ph           -15.294    171.383  -0.089    0.929
## salinity:ph    1.496      7.782   0.192    0.849
## 
## Residual standard error: 34.06 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2411, Adjusted R-squared:  0.1812 
## F-statistic: 4.024 on 3 and 38 DF,  p-value: 0.014
anova (lm1)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  13787 13787.0 11.8856 0.001397 **
## ph           1    172   172.2  0.1484 0.702198   
## salinity:ph  1     43    42.9  0.0370 0.848559   
## Residuals   38  44079  1160.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on density of small thalli, interaction term removed

lm2 <- lm(no.small.fuc.q ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    -152.793        2.508       16.598
summary (lm2)
## 
## Call:
## lm(formula = no.small.fuc.q ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.326 -23.908  -5.857  10.830 105.180 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -152.7933   338.2143  -0.452  0.65394   
## salinity       2.5082     0.7141   3.513  0.00114 **
## ph            16.5983    42.5489   0.390  0.69859   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.64 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2403, Adjusted R-squared:  0.2014 
## F-statistic: 6.169 on 2 and 39 DF,  p-value: 0.0047
anova (lm2)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##           Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity   1  13787 13787.0 12.1865 0.001212 **
## ph         1    172   172.2  0.1522 0.698585   
## Residuals 39  44122  1131.3                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on density of small thalli: daily minimum salinity

lm3 <- lm(no.small.fuc.q ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      -183.477          2.608         21.225
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -44.490 -21.038  -6.786  13.881  99.876 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -183.4772   340.9496  -0.538 0.593795    
## daily.min.sal    2.6080     0.6751   3.863 0.000449 ***
## ph              21.2245    42.9501   0.494 0.624190    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.25 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.293,  Adjusted R-squared:  0.2538 
## F-statistic: 7.461 on 2 and 36 DF,  p-value: 0.001946
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.min.sal  1  16230 16230.4 14.6784 0.0004918 ***
## ph             1    270   270.0  0.2442 0.6241902    
## Residuals     36  39806  1105.7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on density of small thalli: daily maximum salinity

lm4 <- lm(no.small.fuc.q ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      -306.065          3.304         32.422
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.241 -27.190  -6.240   9.158 104.202 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   -306.065    372.207  -0.822  0.41632   
## daily.max.sal    3.304      1.068   3.094  0.00381 **
## ph              32.422     46.226   0.701  0.48758   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35.15 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2101, Adjusted R-squared:  0.1662 
## F-statistic: 4.786 on 2 and 36 DF,  p-value: 0.01435
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.sal  1  11219 11219.5  9.0806 0.004709 **
## ph             1    608   607.8  0.4919 0.487579   
## Residuals     36  44479  1235.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on density of small thalli: daily salinity range

lm5 <- lm(no.small.fuc.q ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         117.659           -3.064           -7.809
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -41.022 -25.359  -9.762  18.193 103.312 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      117.659    369.866   0.318   0.7522  
## daily.sal.range   -3.064      1.213  -2.526   0.0161 *
## ph                -7.809     46.805  -0.167   0.8684  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36.45 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1505, Adjusted R-squared:  0.1033 
## F-statistic:  3.19 on 2 and 36 DF,  p-value: 0.05303
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                 Df Sum Sq Mean Sq F value Pr(>F)  
## daily.sal.range  1   8440  8439.5  6.3521 0.0163 *
## ph               1     37    37.0  0.0278 0.8684  
## Residuals       36  47830  1328.6                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on density of small thalli: daily median salinity

lm6 <- lm(no.small.fuc.q ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      -235.012          2.653         26.894
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.445 -22.694  -6.716  10.877 102.999 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -235.0118   351.1338  -0.669  0.50758    
## daily.med.sal    2.6530     0.7377   3.597  0.00096 ***
## ph              26.8938    44.0903   0.610  0.54571    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.92 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2643, Adjusted R-squared:  0.2235 
## F-statistic: 6.468 on 2 and 36 DF,  p-value: 0.003983
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##               Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.sal  1  14456 14455.6 12.5632 0.001112 **
## ph             1    428   428.1  0.3721 0.545712   
## Residuals     36  41423  1150.6                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 5

lm7 <- lm(no.small.fuc.q ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##           67.3390             0.1074            -3.9749
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.640 -24.536 -19.983   9.136 112.055 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        67.3390   407.1119   0.165    0.869
## min.daily.sal.lt5   0.1074     0.4664   0.230    0.819
## ph                 -3.9749    51.7961  -0.077    0.939
## 
## Residual standard error: 38.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.001359,   Adjusted R-squared:  -0.04985 
## F-statistic: 0.02653 on 2 and 39 DF,  p-value: 0.9738
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1     70   70.15  0.0472 0.8292
## ph                 1      9    8.76  0.0059 0.9392
## Residuals         39  58002 1487.23
plot (lm7)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 10

lm8 <- lm(no.small.fuc.q ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##            -99.199              -0.385              17.979
summary (lm8)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -31.80 -25.43 -20.08  15.77 105.65 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -99.1986   416.3303  -0.238    0.813
## min.daily.sal.lt10  -0.3850     0.4857  -0.793    0.433
## ph                  17.9788    53.0959   0.339    0.737
## 
## Residual standard error: 38.28 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01586,    Adjusted R-squared:  -0.03461 
## F-statistic: 0.3142 on 2 and 39 DF,  p-value: 0.7322
anova (lm8)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1    753  753.01  0.5138 0.4778
## ph                  1    168  168.05  0.1147 0.7367
## Residuals          39  57160 1465.64
plot (lm8)

Effect of salinity and pH on density of small thalli: number of days with a daily minimun salinity less than 15

lm9 <- lm(no.small.fuc.q ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           -94.2296             -0.5522             17.7609
summary (lm9)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34.41 -25.91 -19.30  18.34 102.57 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -94.2296   390.5885  -0.241    0.811
## min.daily.sal.lt15  -0.5522     0.4506  -1.225    0.228
## ph                  17.7609    49.7215   0.357    0.723
## 
## Residual standard error: 37.87 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03707,    Adjusted R-squared:  -0.01231 
## F-statistic: 0.7508 on 2 and 39 DF,  p-value: 0.4787
anova (lm9)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   1970 1970.27  1.3739 0.2482
## ph                  1    183  182.98  0.1276 0.7229
## Residuals          39  55928 1434.05
plot (lm9)

Effect of salinity and pH on percent no.small.fuc.q: number of days with a daily maximum salinity less than 5

lm10 <- lm(no.small.fuc.q ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          60.18350            0.07026           -3.01930
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.805 -24.494 -20.035   9.795 111.699 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        60.18350  418.22060   0.144    0.886
## max.daily.sal.lt5   0.07026    0.47745   0.147    0.884
## ph                 -3.01930   53.26598  -0.057    0.955
## 
## Residual standard error: 38.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0005555,  Adjusted R-squared:  -0.0507 
## F-statistic: 0.01084 on 2 and 39 DF,  p-value: 0.9892
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1     27   27.48  0.0185 0.8926
## ph                 1      5    4.78  0.0032 0.9551
## Residuals         39  58049 1488.43
plot (lm10)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 10

lm11 <- lm(no.small.fuc.q ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           44.71116             0.02656            -0.99326
summary (lm11)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33.89 -24.64 -19.91  10.43 111.21 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         44.71116  417.96578   0.107    0.915
## max.daily.sal.lt10   0.02656    0.48049   0.055    0.956
## ph                  -0.99326   53.24028  -0.019    0.985
## 
## Residual standard error: 38.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  7.888e-05,  Adjusted R-squared:  -0.0512 
## F-statistic: 0.001538 on 2 and 39 DF,  p-value: 0.9985
anova (lm11)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1      4    4.06  0.0027 0.9586
## ph                  1      1    0.52  0.0003 0.9852
## Residuals          39  58076 1489.14
plot (lm11)

Effect of salinity and pH on density of small thalli: number of days with a daily maximum salinity less than 15

lm12 <- lm(no.small.fuc.q ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##            -9.3238             -0.1244              6.0992
summary (lm12)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35.20 -25.88 -18.84  12.47 109.36 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -9.3238   421.6289  -0.022    0.982
## max.daily.sal.lt15  -0.1244     0.4906  -0.254    0.801
## ph                   6.0992    53.7459   0.113    0.910
## 
## Residual standard error: 38.56 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.001646,   Adjusted R-squared:  -0.04955 
## F-statistic: 0.03216 on 2 and 39 DF,  p-value: 0.9684
anova (lm12)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1     76   76.47  0.0514 0.8218
## ph                  1     19   19.15  0.0129 0.9102
## Residuals          39  57985 1486.80
plot (lm12)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 10

lm13 <- lm(no.small.fuc.q ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##              -14.573                -0.171                 6.849
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34.94 -25.87 -19.11  13.15 108.73 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          -14.5733   408.7095  -0.036    0.972
## daily.sal.range.gt10  -0.1710     0.4915  -0.348    0.730
## ph                     6.8493    52.0640   0.132    0.896
## 
## Residual standard error: 38.53 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.003094,   Adjusted R-squared:  -0.04803 
## F-statistic: 0.06051 on 2 and 39 DF,  p-value: 0.9414
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1    154  153.99  0.1037 0.7491
## ph                    1     26   25.69  0.0173 0.8960
## Residuals            39  57901 1484.65
plot (lm13)

Effect of salinity and pH on density of small thalli: number of days with a daily salinity range greater than 5

lm14 <- lm(no.small.fuc.q ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            -47.2864              -0.5375              11.8989
summary (lm14)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -32.04 -26.93 -17.67  17.34 103.00 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -47.2864   382.4803  -0.124    0.902
## daily.sal.range.gt5  -0.5375     0.4471  -1.202    0.237
## ph                   11.8989    48.6295   0.245    0.808
## 
## Residual standard error: 37.9 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03573,    Adjusted R-squared:  -0.01372 
## F-statistic: 0.7226 on 2 and 39 DF,  p-value: 0.4919
anova (lm14)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1   1989 1989.44  1.3854 0.2463
## ph                   1     86   85.98  0.0599 0.8080
## Residuals           39  56006 1436.04
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent density of small thalli: daily minimum ph

lm3 <- lm(no.small.fuc.q ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     -13.6056       -0.9499        2.4765
summary (lm3)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.194 -24.868  -6.745  11.281 105.344 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -13.6056   370.3267  -0.037  0.97088   
## daily.min.ph  -0.9499    47.3143  -0.020  0.98408   
## salinity       2.4765     0.7135   3.471  0.00128 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.7 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2374, Adjusted R-squared:  0.1983 
## F-statistic:  6.07 on 2 and 39 DF,  p-value: 0.00507
anova (lm3)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.min.ph  1    105   105.1  0.0926 0.762573   
## salinity      1  13682 13682.3 12.0471 0.001282 **
## Residuals    39  44294  1135.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on density of small thalli: daily maximum ph

lm4 <- lm(no.small.fuc.q ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     -341.122        39.658         2.602
summary (lm4)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.381 -23.622  -7.539  11.878  89.092 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -341.1220   217.0709  -1.571 0.124151    
## daily.max.ph   39.6584    26.8116   1.479 0.147134    
## salinity        2.6019     0.6971   3.733 0.000604 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.79 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2779, Adjusted R-squared:  0.2409 
## F-statistic: 7.504 on 2 and 39 DF,  p-value: 0.001749
anova (lm4)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.max.ph  1   1156  1156.3  1.0752 0.3061523    
## salinity      1  14984 14983.5 13.9328 0.0006035 ***
## Residuals    39  41941  1075.4                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on density of small thalli: daily ph range

lm5 <- lm(no.small.fuc.q ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        -20.804          -3.688           2.556
summary (lm5)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -41.62 -22.73  -3.88  10.88 105.48 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -20.804     17.221  -1.208 0.234280    
## daily.ph.range   -3.688      3.239  -1.139 0.261816    
## salinity          2.556      0.703   3.636 0.000799 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.15 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2619, Adjusted R-squared:  0.2241 
## F-statistic:  6.92 on 2 and 39 DF,  p-value: 0.00268
anova (lm5)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range  1    679   679.0  0.6177 0.4366425    
## salinity        1  14533 14533.0 13.2214 0.0007989 ***
## Residuals      39  42869  1099.2                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on density of small thalli: daily median ph

lm6 <- lm(no.small.fuc.q ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     -274.633        31.976         2.527
summary (lm6)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -40.495 -23.717  -7.765  11.459 101.857 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -274.6333   311.9059  -0.881 0.383982    
## daily.med.ph   31.9756    39.2661   0.814 0.420400    
## salinity        2.5270     0.7078   3.570 0.000965 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.42 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2501, Adjusted R-squared:  0.2117 
## F-statistic: 6.504 on 2 and 39 DF,  p-value: 0.00365
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##              Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.med.ph  1    291   291.1  0.2607 0.6125424    
## salinity      1  14236 14236.4 12.7480 0.0009653 ***
## Residuals    39  43553  1116.8                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 7

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         -17.9249           -0.3602            2.3933
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.431 -25.043  -7.569  12.120 104.758 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -17.9249    19.9066  -0.900  0.37341   
## min.daily.ph.lt7  -0.3602     1.1042  -0.326  0.74603   
## salinity           2.3933     0.7559   3.166  0.00299 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.65 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2394, Adjusted R-squared:  0.2004 
## F-statistic: 6.139 on 2 and 39 DF,  p-value: 0.004808
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt7  1   2552  2551.9   2.253 0.141407   
## salinity          1  11356 11355.6  10.026 0.002994 **
## Residuals        39  44174  1132.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily minimun ph less than 8

lm7 <- lm(no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##        -21.63396           0.02606           2.47772
summary (lm7)
## 
## Call:
## lm(formula = no.small.fuc.q ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.627 -24.759  -6.982  11.226 105.359 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -21.63396   18.70568  -1.157  0.25449   
## min.daily.ph.lt8   0.02606    0.28599   0.091  0.92786   
## salinity           2.47772    0.71106   3.485  0.00123 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.7 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2375, Adjusted R-squared:  0.1984 
## F-statistic: 6.075 on 2 and 39 DF,  p-value: 0.00505
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.ph.lt8  1      9     9.2  0.0081 0.928714   
## salinity          1  13787 13787.2 12.1419 0.001234 **
## Residuals        39  44285  1135.5                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on density of small thalli: number of days with a daily maximum ph less than 7

lm10 <- lm(no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         -17.9249           -0.3602            2.3933
summary (lm10)
## 
## Call:
## lm(formula = no.small.fuc.q ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -39.431 -25.043  -7.569  12.120 104.758 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      -17.9249    19.9066  -0.900  0.37341   
## max.daily.ph.lt7  -0.3602     1.1042  -0.326  0.74603   
## salinity           2.3933     0.7559   3.166  0.00299 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.65 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2394, Adjusted R-squared:  0.2004 
## F-statistic: 6.139 on 2 and 39 DF,  p-value: 0.004808
anova (lm10)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                  Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.ph.lt7  1   2552  2551.9   2.253 0.141407   
## salinity          1  11356 11355.6  10.026 0.002994 **
## Residuals        39  44174  1132.7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on density of small thalli: number of days with a daily ph range greater than 0.5

lm13 <- lm(no.small.fuc.q ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             -32.0043                0.9839                2.7283
summary (lm13)
## 
## Call:
## lm(formula = no.small.fuc.q ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.198 -22.623  -9.266  14.220  94.110 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -32.0043    19.8146  -1.615 0.114333    
## daily.ph.range.gt0.5   0.9839     0.8777   1.121 0.269188    
## salinity               2.7283     0.7348   3.713 0.000639 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 33.17 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2612, Adjusted R-squared:  0.2233 
## F-statistic: 6.893 on 2 and 39 DF,  p-value: 0.002732
anova (lm13)
## Analysis of Variance Table
## 
## Response: no.small.fuc.q
##                      Df Sum Sq Mean Sq F value    Pr(>F)    
## daily.ph.range.gt0.5  1      0     0.1  0.0001 0.9927205    
## salinity              1  15169 15169.3 13.7865 0.0006391 ***
## Residuals            39  42912  1100.3                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q2. Effects of salinity and pH on reproductive effort#### Data I have for reproductive effort: covcl.repro (cover class of reproductive tissue), dw.repro (dry weight of reproductive tissue), apices.repro (number of reproductive apices), perc.ra (percent of apices that are reproductive), avg.oog (average number of oogonia) and perc.rdw (percent of dry weight that is reproductive tissue)

Data for salinity: salinity (median salinity), daily.min.sal (daily minimun salinity), daily.max.sal (daily maximum salinity), daily.sal.range (daily salinity range), daily.med.sal (daily median salinity), min.daily.sal.lt5/10/15 (number of days with a minimun daily dalinity less than 5, 10, and 15), max.daily.sal.lt5/10/15 (number of days with a maximum daily salinity less than 5, 10, and 15) and daily.sal.range.gt5/10 (number of days with a daily salinity range greater than 5 and 10)

Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5) ####Q2.1 Effects of salinity and pH on cover class of reproductive tissue#### Different salinity terms first

Effect of pH and salinity on cover class of reproductive tissue

lm1 <- lm(covcl.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     0.44840      0.42877      0.35997     -0.06006
summary (lm1)
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.54010 -0.83958  0.05536  0.65234  2.23601 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.44840   37.51407   0.012    0.991
## salinity     0.42877    1.66433   0.258    0.798
## ph           0.35997    4.74161   0.076    0.940
## salinity:ph -0.06006    0.21049  -0.285    0.777
## 
## Residual standard error: 0.9804 on 41 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1199, Adjusted R-squared:  0.05548 
## F-statistic: 1.861 on 3 and 41 DF,  p-value: 0.1512
anova (lm1)
## Analysis of Variance Table
## 
## Response: covcl.repro
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity     1  4.610  4.6104  4.7964 0.03426 *
## ph           1  0.679  0.6793  0.7067 0.40543  
## salinity:ph  1  0.078  0.0782  0.0814 0.77684  
## Residuals   41 39.410  0.9612                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on cover class of reproductive tissue, interaction term removed

lm2 <- lm(covcl.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    10.83897     -0.04605     -0.95358
summary (lm2)
## 
## Call:
## lm(formula = covcl.repro ~ salinity + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.56619 -0.84130  0.05279  0.65350  2.22484 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 10.83897    8.90187   1.218   0.2302  
## salinity    -0.04605    0.02030  -2.268   0.0285 *
## ph          -0.95358    1.12188  -0.850   0.4002  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9696 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1181, Adjusted R-squared:  0.07614 
## F-statistic: 2.813 on 2 and 42 DF,  p-value: 0.07136
anova (lm2)
## Analysis of Variance Table
## 
## Response: covcl.repro
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity   1  4.610  4.6104  4.9037 0.03228 *
## ph         1  0.679  0.6793  0.7225 0.40015  
## Residuals 42 39.488  0.9402                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on cover class of reproductive tissue: daily minimum salinity

lm3 <- lm(covcl.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##       9.33908       -0.04359       -0.78329
summary (lm3)
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.55591 -0.83056  0.03966  0.66872  2.25303 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    9.33908    9.21917   1.013    0.317  
## daily.min.sal -0.04359    0.01972  -2.211    0.033 *
## ph            -0.78329    1.16350  -0.673    0.505  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9885 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1164, Adjusted R-squared:  0.07109 
## F-statistic: 2.569 on 2 and 39 DF,  p-value: 0.08952
anova (lm3)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1  4.577  4.5772  4.6848 0.03661 *
## ph             1  0.443  0.4428  0.4532 0.50478  
## Residuals     39 38.105  0.9770                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on cover class of reproductive tissue: daily maximum salinity

lm4 <- lm(covcl.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      10.39601       -0.04172       -0.89094
summary (lm4)
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.51685 -0.98552 -0.07688  0.84078  2.44276 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   10.39601    9.83251   1.057    0.297
## daily.max.sal -0.04172    0.03053  -1.366    0.180
## ph            -0.89094    1.22313  -0.728    0.471
## 
## Residual standard error: 1.024 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05112,    Adjusted R-squared:  0.002458 
## F-statistic: 1.051 on 2 and 39 DF,  p-value: 0.3594
anova (lm4)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1  1.648 1.64780  1.5705 0.2176
## ph             1  0.557 0.55671  0.5306 0.4707
## Residuals     39 40.920 1.04924
plot (lm4)

Effect and salinity and pH on cover class of reproductive tissue: daily salinity range

lm5 <- lm(covcl.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         4.41864          0.07129         -0.32778
summary (lm5)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44900 -0.90916  0.07011  0.66383  2.53884 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      4.41864    9.22764   0.479   0.6347  
## daily.sal.range  0.07129    0.03262   2.186   0.0349 *
## ph              -0.32778    1.16718  -0.281   0.7803  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9897 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1142, Adjusted R-squared:  0.06877 
## F-statistic: 2.514 on 2 and 39 DF,  p-value: 0.094
anova (lm5)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  4.847  4.8473  4.9487 0.03197 *
## ph               1  0.077  0.0772  0.0789 0.78033  
## Residuals       39 38.200  0.9795                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on cover class of reproductive tissue: daily median salinity

lm6 <- lm(covcl.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##       10.1100        -0.0423        -0.8726
summary (lm6)
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.57480 -0.89233 -0.00094  0.69612  2.24551 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   10.11004    9.39655   1.076   0.2886  
## daily.med.sal -0.04230    0.02133  -1.983   0.0544 .
## ph            -0.87259    1.18188  -0.738   0.4647  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9994 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09677,    Adjusted R-squared:  0.05046 
## F-statistic: 2.089 on 2 and 39 DF,  p-value: 0.1374
anova (lm6)
## Analysis of Variance Table
## 
## Response: covcl.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1  3.629  3.6290  3.6335 0.06402 .
## ph             1  0.544  0.5444  0.5451 0.46475  
## Residuals     39 38.952  0.9988                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 5

lm7 <- lm(covcl.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##           2.14378           -0.01937            0.03803
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42864 -0.72749  0.07821  0.57764  2.58277 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        2.14378    9.85009   0.218    0.829
## min.daily.sal.lt5 -0.01937    0.01194  -1.623    0.112
## ph                 0.03803    1.25475   0.030    0.976
## 
## Residual standard error: 0.9966 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.06848,    Adjusted R-squared:  0.02412 
## F-statistic: 1.544 on 2 and 42 DF,  p-value: 0.2254
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1  3.065 3.06550  3.0867 0.08622 .
## ph                 1  0.001 0.00091  0.0009 0.97597  
## Residuals         42 41.711 0.99313                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 10

lm8 <- lm(covcl.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##            7.39001            -0.00259            -0.65016
summary (lm8)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3786 -1.1162 -0.1138  0.7066  2.8761 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         7.39001   10.49010   0.704    0.485
## min.daily.sal.lt10 -0.00259    0.01290  -0.201    0.842
## ph                 -0.65016    1.33962  -0.485    0.630
## 
## Residual standard error: 1.027 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01103,    Adjusted R-squared:  -0.03606 
## F-statistic: 0.2343 on 2 and 42 DF,  p-value: 0.7921
anova (lm8)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1  0.246 0.24574  0.2331 0.6318
## ph                  1  0.248 0.24836  0.2355 0.6300
## Residuals          42 44.284 1.05437
plot (lm8)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily minimun salinity less than 15

lm9 <- lm(covcl.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           9.597755            0.004594           -0.943574
summary (lm9)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4195 -1.0538 -0.1273  0.7745  2.8176 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         9.597755   9.902351   0.969    0.338
## min.daily.sal.lt15  0.004594   0.012018   0.382    0.704
## ph                 -0.943574   1.262584  -0.747    0.459
## 
## Residual standard error: 1.026 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01352,    Adjusted R-squared:  -0.03346 
## F-statistic: 0.2877 on 2 and 42 DF,  p-value: 0.7514
anova (lm9)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1  0.018 0.01787  0.0170 0.8969
## ph                  1  0.587 0.58740  0.5585 0.4590
## Residuals          42 44.173 1.05173
plot (lm9)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 5

lm10 <- lm(covcl.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##           2.57645           -0.01571           -0.02104
summary (lm10)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.39874 -0.82893  0.03388  0.60410  2.60757 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        2.57645   10.24593   0.251    0.803
## max.daily.sal.lt5 -0.01571    0.01237  -1.270    0.211
## ph                -0.02104    1.30652  -0.016    0.987
## 
## Residual standard error: 1.008 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04669,    Adjusted R-squared:  0.00129 
## F-statistic: 1.028 on 2 and 42 DF,  p-value: 0.3664
anova (lm10)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                   Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1  2.090 2.09025  2.0566 0.1590
## ph                 1  0.000 0.00026  0.0003 0.9872
## Residuals         42 42.687 1.01636
plot (lm10)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 10

lm11 <- lm(covcl.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          2.4221975          -0.0162683          -0.0001903
summary (lm11)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.40446 -0.81877  0.03486  0.59556  2.59559 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)         2.4221975 10.2290847   0.237    0.814
## max.daily.sal.lt10 -0.0162683  0.0124313  -1.309    0.198
## ph                 -0.0001903  1.3045722   0.000    1.000
## 
## Residual standard error: 1.007 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04887,    Adjusted R-squared:  0.003576 
## F-statistic: 1.079 on 2 and 42 DF,  p-value: 0.3492
anova (lm11)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1  2.188  2.1882  2.1579 0.1493
## ph                  1  0.000  0.0000  0.0000 0.9999
## Residuals          42 42.590  1.0140
plot (lm11)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily maximum salinity less than 15

lm12 <- lm(covcl.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##            2.76494            -0.01486            -0.04445
summary (lm12)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.40779 -0.86386  0.01127  0.59821  2.69472 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         2.76494   10.38024   0.266    0.791
## max.daily.sal.lt15 -0.01486    0.01275  -1.166    0.250
## ph                 -0.04445    1.32486  -0.034    0.973
## 
## Residual standard error: 1.011 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.04111,    Adjusted R-squared:  -0.004553 
## F-statistic: 0.9003 on 2 and 42 DF,  p-value: 0.4142
anova (lm12)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                    Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1  1.840 1.83960  1.7995 0.1870
## ph                  1  0.001 0.00115  0.0011 0.9734
## Residuals          42 42.937 1.02231
plot (lm12)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily salinity range greater than 10

lm13 <- lm(covcl.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             5.987381             -0.007387             -0.464675
summary (lm13)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.36482 -1.04312 -0.05575  0.66306  2.81128 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)           5.987381  10.191191   0.588    0.560
## daily.sal.range.gt10 -0.007387   0.012907  -0.572    0.570
## ph                   -0.464675   1.300051  -0.357    0.723
## 
## Residual standard error: 1.023 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01775,    Adjusted R-squared:  -0.02903 
## F-statistic: 0.3794 on 2 and 42 DF,  p-value: 0.6866
anova (lm13)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1  0.661 0.66083  0.6310 0.4314
## ph                    1  0.134 0.13379  0.1278 0.7226
## Residuals            42 43.983 1.04722
plot (lm13)

Effect of salinity and pH on cover class of reproductive tissue: number of days with a daily salinity range greater than 5

lm14 <- lm(covcl.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            7.736780            -0.004199            -0.689381
summary (lm14)
## 
## Call:
## lm(formula = covcl.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.36920 -1.10416 -0.09996  0.67216  2.89584 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)          7.736780   9.498784   0.815    0.420
## daily.sal.range.gt5 -0.004199   0.011783  -0.356    0.723
## ph                  -0.689381   1.208386  -0.570    0.571
## 
## Residual standard error: 1.026 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.01307,    Adjusted R-squared:  -0.03393 
## F-statistic: 0.2781 on 2 and 42 DF,  p-value: 0.7586
anova (lm14)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                     Df Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1  0.243 0.24275  0.2307 0.6335
## ph                   1  0.342 0.34246  0.3255 0.5714
## Residuals           42 44.193 1.05220
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on cover class of reproductive tissue: daily minimum ph

lm3 <- lm(covcl.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##      4.18295      -0.11496      -0.04498
summary (lm3)
## 
## Call:
## lm(formula = covcl.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.41212 -0.92758  0.03605  0.70758  2.28876 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   4.18295   10.64235   0.393   0.6963  
## daily.min.ph -0.11496    1.36026  -0.085   0.9330  
## salinity     -0.04498    0.02049  -2.196   0.0337 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9779 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1031, Adjusted R-squared:  0.06041 
## F-statistic: 2.414 on 2 and 42 DF,  p-value: 0.1017
anova (lm3)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value Pr(>F)  
## daily.min.ph  1  0.008  0.0077  0.0081 0.9289  
## salinity      1  4.610  4.6095  4.8206 0.0337 *
## Residuals    42 40.161  0.9562                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on cover class of reproductive tissue: daily maximum ph

lm4 <- lm(covcl.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     10.94438      -0.94964      -0.04734
summary (lm4)
## 
## Call:
## lm(formula = covcl.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.58096 -0.84162  0.02225  0.72491  2.19505 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  10.94438    6.10897   1.792   0.0804 .
## daily.max.ph -0.94964    0.75486  -1.258   0.2153  
## salinity     -0.04734    0.02015  -2.350   0.0236 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.96 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1355, Adjusted R-squared:  0.09437 
## F-statistic: 3.293 on 2 and 42 DF,  p-value: 0.04696
anova (lm4)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.ph  1  0.981  0.9811  1.0645 0.30810  
## salinity      1  5.088  5.0879  5.5205 0.02357 *
## Residuals    42 38.709  0.9216                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on cover class of reproductive tissue: daily ph range

lm5 <- lm(covcl.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        3.28083         0.05096        -0.04586
summary (lm5)
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.36586 -0.91996  0.06476  0.68493  2.32533 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     3.28083    0.50364   6.514 7.23e-08 ***
## daily.ph.range  0.05096    0.09506   0.536   0.5947    
## salinity       -0.04586    0.02044  -2.243   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9746 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1091, Adjusted R-squared:  0.06663 
## F-statistic: 2.571 on 2 and 42 DF,  p-value: 0.08848
anova (lm5)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                Df Sum Sq Mean Sq F value Pr(>F)  
## daily.ph.range  1  0.103  0.1026   0.108 0.7440  
## salinity        1  4.781  4.7807   5.033 0.0302 *
## Residuals      42 39.894  0.9499                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect salinity and pH on cover class of reproductive tissue: daily median ph

lm6 <- lm(covcl.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##      9.63859      -0.80213      -0.04571
summary (lm6)
## 
## Call:
## lm(formula = covcl.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.53881 -0.85267  0.04676  0.68040  2.21041 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   9.63859    8.71860   1.106   0.2752  
## daily.med.ph -0.80213    1.09879  -0.730   0.4694  
## salinity     -0.04571    0.02033  -2.249   0.0298 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9718 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1142, Adjusted R-squared:  0.07202 
## F-statistic: 2.707 on 2 and 42 DF,  p-value: 0.07835
anova (lm6)
## Analysis of Variance Table
## 
## Response: covcl.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.ph  1  0.339  0.3390  0.3590 0.55230  
## salinity      1  4.775  4.7747  5.0558 0.02985 *
## Residuals    42 39.664  0.9444                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily minimun ph less than 7

lm7 <- lm(covcl.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          3.11143           0.02015          -0.04007
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.32217 -0.91782 -0.01232  0.70541  2.41136 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.11143    0.57233   5.436 2.56e-06 ***
## min.daily.ph.lt7  0.02015    0.03181   0.634   0.5298    
## salinity         -0.04007    0.02168  -1.848   0.0716 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9733 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1115, Adjusted R-squared:  0.06914 
## F-statistic: 2.634 on 2 and 42 DF,  p-value: 0.08361
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)  
## min.daily.ph.lt7  1  1.754  1.7542  1.8517 0.1808  
## salinity          1  3.236  3.2365  3.4165 0.0716 .
## Residuals        42 39.787  0.9473                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily minimun ph less than 8

lm7 <- lm(covcl.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          3.42009          -0.00576          -0.04498
summary (lm7)
## 
## Call:
## lm(formula = covcl.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.27655 -0.84816  0.06527  0.76202  2.37377 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.42009    0.53699   6.369 1.17e-07 ***
## min.daily.ph.lt8 -0.00576    0.00807  -0.714   0.4793    
## salinity         -0.04498    0.02030  -2.216   0.0322 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9721 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1137, Adjusted R-squared:  0.07151 
## F-statistic: 2.694 on 2 and 42 DF,  p-value: 0.07926
anova (lm7)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt8  1  0.453  0.4527  0.4791 0.49263  
## salinity          1  4.639  4.6391  4.9096 0.03218 *
## Residuals        42 39.686  0.9449                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily maximum ph less than 7

lm10 <- lm(covcl.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          3.11143           0.02015          -0.04007
summary (lm10)
## 
## Call:
## lm(formula = covcl.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.32217 -0.91782 -0.01232  0.70541  2.41136 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       3.11143    0.57233   5.436 2.56e-06 ***
## max.daily.ph.lt7  0.02015    0.03181   0.634   0.5298    
## salinity         -0.04007    0.02168  -1.848   0.0716 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9733 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1115, Adjusted R-squared:  0.06914 
## F-statistic: 2.634 on 2 and 42 DF,  p-value: 0.08361
anova (lm10)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)  
## max.daily.ph.lt7  1  1.754  1.7542  1.8517 0.1808  
## salinity          1  3.236  3.2365  3.4165 0.0716 .
## Residuals        42 39.787  0.9473                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect salinity and pH on cover class of reproductive tissue: number of days with a daily ph range greater than 0.5

lm13 <- lm(covcl.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##             3.387132             -0.009277             -0.047095
summary (lm13)
## 
## Call:
## lm(formula = covcl.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4265 -0.9674  0.0300  0.7377  2.2696 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)           3.387132   0.575693   5.884 5.84e-07 ***
## daily.ph.range.gt0.5 -0.009277   0.025071  -0.370   0.7132    
## salinity             -0.047095   0.021280  -2.213   0.0324 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9763 on 42 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.1059, Adjusted R-squared:  0.0633 
## F-statistic: 2.487 on 2 and 42 DF,  p-value: 0.09536
anova (lm13)
## Analysis of Variance Table
## 
## Response: covcl.repro
##                      Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range.gt0.5  1  0.072  0.0718  0.0753 0.78511  
## salinity              1  4.669  4.6691  4.8980 0.03237 *
## Residuals            42 40.037  0.9533                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

####Q2.2 Effects of salinity and pH on dry weight of reproductive tissue#### Different salinity terms first

Effect of pH and salinity on dry weight of reproductive tissue

lm1 <- lm(dw.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    29.13342     -0.23620     -3.58860      0.03165
summary (lm1)
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4361 -0.7506 -0.3286  0.6013  3.0755 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 29.13342   43.00357   0.677    0.502
## salinity    -0.23620    1.92164  -0.123    0.903
## ph          -3.58860    5.43627  -0.660    0.513
## salinity:ph  0.03165    0.24310   0.130    0.897
## 
## Residual standard error: 1.11 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1289, Adjusted R-squared:  0.06009 
## F-statistic: 1.874 on 3 and 38 DF,  p-value: 0.1505
anova (lm1)
## Analysis of Variance Table
## 
## Response: dw.repro
##             Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity     1  0.751  0.7507  0.6093 0.43989  
## ph           1  6.154  6.1541  4.9951 0.03138 *
## salinity:ph  1  0.021  0.0209  0.0169 0.89711  
## Residuals   38 46.817  1.2320                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on dry weight of reproductive tissue, interaction term removed

lm2 <- lm(dw.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    23.69833      0.01394     -2.90138
summary (lm2)
## 
## Call:
## lm(formula = dw.repro ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3749 -0.7483 -0.3525  0.5908  3.1315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 23.69833   10.17504   2.329   0.0251 *
## salinity     0.01394    0.02326   0.599   0.5525  
## ph          -2.90138    1.28171  -2.264   0.0292 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.096 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1285, Adjusted R-squared:  0.08378 
## F-statistic: 2.875 on 2 and 39 DF,  p-value: 0.06846
anova (lm2)
## Analysis of Variance Table
## 
## Response: dw.repro
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity   1  0.751  0.7507  0.6251 0.43396  
## ph         1  6.154  6.1541  5.1242 0.02923 *
## Residuals 39 46.838  1.2010                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on dry weight of reproductive tissue: daily minimum salinity

lm3 <- lm(dw.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = dw.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     23.508316       0.008994      -2.860912
summary (lm3)
## 
## Call:
## lm(formula = dw.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4088 -0.7541 -0.4795  0.6086  3.0980 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   23.508316  10.671121   2.203   0.0341 *
## daily.min.sal  0.008994   0.022979   0.391   0.6978  
## ph            -2.860912   1.345801  -2.126   0.0404 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.129 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1196, Adjusted R-squared:  0.07068 
## F-statistic: 2.445 on 2 and 36 DF,  p-value: 0.101
anova (lm3)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1  0.473  0.4728  0.3710 0.54629  
## ph             1  5.759  5.7591  4.5191 0.04045 *
## Residuals     36 45.878  1.2744                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on dry weight of reproductive tissue: daily maximum salinity

lm4 <- lm(dw.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = dw.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      21.61833        0.02819       -2.69295
summary (lm4)
## 
## Call:
## lm(formula = dw.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2803 -0.7569 -0.3105  0.4818  3.2274 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   21.61833   10.90360   1.983   0.0551 .
## daily.max.sal  0.02819    0.03396   0.830   0.4119  
## ph            -2.69295    1.35554  -1.987   0.0546 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.121 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1325, Adjusted R-squared:  0.08425 
## F-statistic: 2.748 on 2 and 36 DF,  p-value: 0.0775
anova (lm4)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.sal  1  1.946  1.9459  1.5495 0.22125  
## ph             1  4.956  4.9562  3.9467 0.05462 .
## Residuals     36 45.208  1.2558                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on dry weight of reproductive tissue: daily salinity range

lm5 <- lm(dw.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        23.71741          0.01421         -2.87473
summary (lm5)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5687 -0.6877 -0.4659  0.4886  2.9610 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     23.71741   10.61231   2.235   0.0317 *
## daily.sal.range  0.01421    0.03769   0.377   0.7083  
## ph              -2.87473    1.34319  -2.140   0.0392 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.129 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1193, Adjusted R-squared:  0.0704 
## F-statistic: 2.439 on 2 and 36 DF,  p-value: 0.1016
anova (lm5)
## Analysis of Variance Table
## 
## Response: dw.repro
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  0.379  0.3787  0.2971 0.58909  
## ph               1  5.839  5.8393  4.5806 0.03918 *
## Residuals       36 45.892  1.2748                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on dry weight of reproductive tissue: daily median salinity

lm6 <- lm(dw.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = dw.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      22.73780        0.01629       -2.78686
summary (lm6)
## 
## Call:
## lm(formula = dw.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3156 -0.7908 -0.3855  0.5797  3.1840 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   22.73780   10.71738   2.122   0.0408 *
## daily.med.sal  0.01629    0.02446   0.666   0.5097  
## ph            -2.78686    1.34720  -2.069   0.0458 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.124 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1266, Adjusted R-squared:  0.07808 
## F-statistic: 2.609 on 2 and 36 DF,  p-value: 0.08746
anova (lm6)
## Analysis of Variance Table
## 
## Response: dw.repro
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1  1.187  1.1873  0.9391 0.33896  
## ph             1  5.410  5.4101  4.2793 0.04582 *
## Residuals     36 45.513  1.2642                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 5

lm7 <- lm(dw.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##         23.144569          -0.004583          -2.783073
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5453 -0.7510 -0.5139  0.5368  2.9677 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       23.144569  10.903640   2.123   0.0402 *
## min.daily.sal.lt5 -0.004583   0.013496  -0.340   0.7360  
## ph                -2.783073   1.389178  -2.003   0.0521 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.099 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.123,  Adjusted R-squared:  0.07808 
## F-statistic: 2.736 on 2 and 39 DF,  p-value: 0.07727
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1  1.763  1.7627  1.4586 0.23442  
## ph                 1  4.850  4.8503  4.0136 0.05212 .
## Residuals         39 47.130  1.2085                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 10

lm8 <- lm(dw.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          22.828587           -0.004819           -2.740937
summary (lm8)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5099 -0.7636 -0.4957  0.5613  3.0076 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        22.828587  11.292281   2.022   0.0501 .
## min.daily.sal.lt10 -0.004819   0.014238  -0.338   0.7368  
## ph                 -2.740937   1.442685  -1.900   0.0649 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.099 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.123,  Adjusted R-squared:  0.07806 
## F-statistic: 2.736 on 2 and 39 DF,  p-value: 0.0773
anova (lm8)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt10  1  2.250  2.2500  1.8618 0.18024  
## ph                  1  4.362  4.3621  3.6096 0.06486 .
## Residuals          39 47.131  1.2085                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun salinity less than 15

lm9 <- lm(dw.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##         24.3257362          -0.0007953          -2.9378646
summary (lm9)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5270 -0.7207 -0.5054  0.6026  2.9993 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        24.3257362 10.6905055   2.275   0.0285 *
## min.daily.sal.lt15 -0.0007953  0.0133317  -0.060   0.9527  
## ph                 -2.9378646  1.3635212  -2.155   0.0374 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.101 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1205, Adjusted R-squared:  0.07543 
## F-statistic: 2.673 on 2 and 39 DF,  p-value: 0.08171
anova (lm9)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt15  1  0.852  0.8517  0.7028 0.40696  
## ph                  1  5.626  5.6262  4.6424 0.03743 *
## Residuals          39 47.265  1.2119                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 5

lm10 <- lm(dw.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          23.73076           -0.00228           -2.86057
summary (lm10)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5417 -0.7292 -0.4983  0.5745  2.9759 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       23.73076   11.22706   2.114   0.0410 *
## max.daily.sal.lt5 -0.00228    0.01385  -0.165   0.8701  
## ph                -2.86057    1.43198  -1.998   0.0528 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.101 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1211, Adjusted R-squared:  0.07599 
## F-statistic: 2.686 on 2 and 39 DF,  p-value: 0.08075
anova (lm10)
## Analysis of Variance Table
## 
## Response: dw.repro
##                   Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt5  1  1.673  1.6732  1.3814 0.24698  
## ph                 1  4.833  4.8333  3.9905 0.05277 .
## Residuals         39 47.237  1.2112                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 10

lm11 <- lm(dw.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          23.118459           -0.004061           -2.780111
summary (lm11)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5471 -0.7488 -0.4920  0.5488  2.9705 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        23.118459  11.214668   2.061   0.0460 *
## max.daily.sal.lt10 -0.004061   0.013929  -0.292   0.7722  
## ph                 -2.780111   1.430642  -1.943   0.0592 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.1 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1224, Adjusted R-squared:  0.07736 
## F-statistic: 2.719 on 2 and 39 DF,  p-value: 0.07845
anova (lm11)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1  2.009  2.0094  1.6615 0.20500  
## ph                  1  4.567  4.5670  3.7763 0.05923 .
## Residuals          39 47.167  1.2094                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum salinity less than 15

lm12 <- lm(dw.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##          21.799386           -0.007591           -2.606681
summary (lm12)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5371 -0.7683 -0.4690  0.5256  2.9577 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        21.799386  11.312447   1.927   0.0613 .
## max.daily.sal.lt15 -0.007591   0.014222  -0.534   0.5965  
## ph                 -2.606681   1.444329  -1.805   0.0788 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.097 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1268, Adjusted R-squared:  0.08206 
## F-statistic: 2.833 on 2 and 39 DF,  p-value: 0.07102
anova (lm12)
## Analysis of Variance Table
## 
## Response: dw.repro
##                    Df Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt15  1  2.897  2.8972  2.4078 0.12881  
## ph                  1  3.919  3.9192  3.2572 0.07884 .
## Residuals          39 46.927  1.2032                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily salinity range greater than 10

lm13 <- lm(dw.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##            24.077915             -0.001496             -2.905509
summary (lm13)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5256 -0.7225 -0.5028  0.5957  2.9920 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          24.077915  11.011173   2.187   0.0348 *
## daily.sal.range.gt10 -0.001496   0.014311  -0.105   0.9173  
## ph                   -2.905509   1.405082  -2.068   0.0453 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.101 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1207, Adjusted R-squared:  0.07561 
## F-statistic: 2.677 on 2 and 39 DF,  p-value: 0.08141
anova (lm13)
## Analysis of Variance Table
## 
## Response: dw.repro
##                      Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt10  1  1.306  1.3056  1.0775 0.30565  
## ph                    1  5.181  5.1813  4.2760 0.04533 *
## Residuals            39 47.256  1.2117                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on dry weight of reproductive tissue: number of days with a daily salinity range greater than 5

lm14 <- lm(dw.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##          24.4145764           -0.0009184           -2.9486754
summary (lm14)
## 
## Call:
## lm(formula = dw.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5310 -0.7171 -0.5069  0.5994  2.9976 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         24.4145764 10.2555724   2.381   0.0223 *
## daily.sal.range.gt5 -0.0009184  0.0129238  -0.071   0.9437  
## ph                  -2.9486754  1.3044237  -2.261   0.0294 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.101 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1206, Adjusted R-squared:  0.07547 
## F-statistic: 2.673 on 2 and 39 DF,  p-value: 0.08165
anova (lm14)
## Analysis of Variance Table
## 
## Response: dw.repro
##                     Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range.gt5  1  0.287  0.2871  0.2369 0.62917  
## ph                   1  6.193  6.1926  5.1099 0.02945 *
## Residuals           39 47.263  1.2119                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on dry weight of reproductive tissue: daily minimum ph

lm3 <- lm(dw.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = dw.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     23.15752      -2.87063       0.01381
summary (lm3)
## 
## Call:
## lm(formula = dw.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3611 -0.7251 -0.3861  0.5389  2.8630 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  23.15752   12.47810   1.856   0.0710 .
## daily.min.ph -2.87063    1.59338  -1.802   0.0793 .
## salinity      0.01381    0.02382   0.580   0.5653  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08973,    Adjusted R-squared:  0.04304 
## F-statistic: 1.922 on 2 and 39 DF,  p-value: 0.1599
anova (lm3)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  4.400  4.4003  3.5079 0.06859 .
## salinity      1  0.422  0.4218  0.3363 0.56532  
## Residuals    39 48.921  1.2544                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on dry weight of reproductive tissue: daily maximum ph

lm4 <- lm(dw.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = dw.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##      9.97998      -1.15051       0.01536
summary (lm4)
## 
## Call:
## lm(formula = dw.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2149 -0.7934 -0.3205  0.4762  3.4639 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)   9.97998    7.41154   1.347    0.186
## daily.max.ph -1.15051    0.91606  -1.256    0.217
## salinity      0.01536    0.02429   0.632    0.531
## 
## Residual standard error: 1.143 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0523, Adjusted R-squared:  0.003698 
## F-statistic: 1.076 on 2 and 39 DF,  p-value: 0.3508
anova (lm4)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1  2.288 2.28837  1.7523 0.1933
## salinity      1  0.522 0.52231  0.3999 0.5308
## Residuals    39 50.932 1.30596
plot (lm4)

Effect of salinity and pH on dry weight of reproductive tissue: daily ph range

lm5 <- lm(dw.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        0.68748         0.06779         0.01780
summary (lm5)
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1090 -0.7310 -0.4039  0.3565  3.7290 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.68748    0.60509   1.136    0.263
## daily.ph.range  0.06779    0.16106   0.421    0.676
## salinity        0.01780    0.02463   0.723    0.474
## 
## Residual standard error: 1.163 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01843,    Adjusted R-squared:  -0.03191 
## F-statistic: 0.3661 on 2 and 39 DF,  p-value: 0.6958
anova (lm5)
## Analysis of Variance Table
## 
## Response: dw.repro
##                Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1  0.284 0.28374  0.2098 0.6495
## salinity        1  0.707 0.70658  0.5224 0.4741
## Residuals      39 52.753 1.35263
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on dry weight of reproductive tissue: daily median ph

lm6 <- lm(dw.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = dw.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     18.40177      -2.23365       0.01559
summary (lm6)
## 
## Call:
## lm(formula = dw.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2410 -0.7763 -0.3866  0.5749  3.2641 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  18.40177   10.22272   1.800   0.0796 .
## daily.med.ph -2.23365    1.28793  -1.734   0.0908 .
## salinity      0.01559    0.02381   0.655   0.5165  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.123 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08457,    Adjusted R-squared:  0.03762 
## F-statistic: 1.801 on 2 and 39 DF,  p-value: 0.1785
anova (lm6)
## Analysis of Variance Table
## 
## Response: dw.repro
##              Df Sum Sq Mean Sq F value Pr(>F)  
## daily.med.ph  1  4.004  4.0043  3.1742 0.0826 .
## salinity      1  0.541  0.5407  0.4286 0.5165  
## Residuals    39 49.198  1.2615                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun ph less than 7

lm7 <- lm(dw.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          0.98621          -0.03313           0.01059
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1520 -0.8259 -0.3991  0.3997  3.5814 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.98621    0.68223   1.446    0.156
## min.daily.ph.lt7 -0.03313    0.03784  -0.875    0.387
## salinity          0.01059    0.02597   0.408    0.686
## 
## Residual standard error: 1.154 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03297,    Adjusted R-squared:  -0.01662 
## F-statistic: 0.6649 on 2 and 39 DF,  p-value: 0.5201
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1  1.550 1.55038  1.1634 0.2874
## salinity          1  0.222 0.22161  0.1663 0.6856
## Residuals        39 51.971 1.33259
plot (lm7)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily minimun ph less than 8

lm7 <- lm(dw.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##         0.829083         -0.005451          0.018162
summary (lm7)
## 
## Call:
## lm(formula = dw.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0741 -0.7589 -0.4176  0.4424  3.7772 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.829083   0.647042   1.281    0.208
## min.daily.ph.lt8 -0.005451   0.009990  -0.546    0.588
## salinity          0.018162   0.024564   0.739    0.464
## 
## Residual standard error: 1.161 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02144,    Adjusted R-squared:  -0.02875 
## F-statistic: 0.4272 on 2 and 39 DF,  p-value: 0.6554
anova (lm7)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1  0.415 0.41485  0.3076 0.5823
## salinity          1  0.737 0.73720  0.5467 0.4641
## Residuals        39 52.591 1.34849
plot (lm7)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily maximum ph less than 7

lm10 <- lm(dw.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = dw.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          0.98621          -0.03313           0.01059
summary (lm10)
## 
## Call:
## lm(formula = dw.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1520 -0.8259 -0.3991  0.3997  3.5814 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       0.98621    0.68223   1.446    0.156
## max.daily.ph.lt7 -0.03313    0.03784  -0.875    0.387
## salinity          0.01059    0.02597   0.408    0.686
## 
## Residual standard error: 1.154 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03297,    Adjusted R-squared:  -0.01662 
## F-statistic: 0.6649 on 2 and 39 DF,  p-value: 0.5201
anova (lm10)
## Analysis of Variance Table
## 
## Response: dw.repro
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1  1.550 1.55038  1.1634 0.2874
## salinity          1  0.222 0.22161  0.1663 0.6856
## Residuals        39 51.971 1.33259
plot (lm10)

Effect salinity and pH on dry weight of reproductive tissue: number of days with a daily ph range greater than 0.5

lm13 <- lm(dw.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              1.08171              -0.03480               0.01038
summary (lm13)
## 
## Call:
## lm(formula = dw.repro ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.1560 -0.8290 -0.3845  0.4002  3.5253 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           1.08171    0.67781   1.596    0.119
## daily.ph.range.gt0.5 -0.03480    0.02968  -1.172    0.248
## salinity              0.01038    0.02516   0.412    0.682
## 
## Residual standard error: 1.146 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04754,    Adjusted R-squared:  -0.001308 
## F-statistic: 0.9732 on 2 and 39 DF,  p-value: 0.3868
anova (lm13)
## Analysis of Variance Table
## 
## Response: dw.repro
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1  2.331 2.33150  1.7764 0.1903
## salinity              1  0.223 0.22327  0.1701 0.6823
## Residuals            39 51.188 1.31252
plot (lm13)

####Q2.3 Effects of salinity and pH on number of reproductive apices#### Different salinity terms first

Effect of pH and salinity on number of reproductive apices

lm1 <- lm(apices.repro ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     598.588      -13.460      -72.716        1.723
summary (lm1)
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.018 -19.911  -7.709  13.912  63.213 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  598.588    985.195   0.608    0.547
## salinity     -13.460     44.024  -0.306    0.761
## ph           -72.716    124.543  -0.584    0.563
## salinity:ph    1.723      5.569   0.309    0.759
## 
## Residual standard error: 25.43 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04191,    Adjusted R-squared:  -0.03372 
## F-statistic: 0.5541 on 3 and 38 DF,  p-value: 0.6485
anova (lm1)
## Analysis of Variance Table
## 
## Response: apices.repro
##             Df  Sum Sq Mean Sq F value Pr(>F)
## salinity     1   102.4  102.41  0.1584 0.6929
## ph           1   910.7  910.67  1.4083 0.2427
## salinity:ph  1    61.9   61.91  0.0957 0.7587
## Residuals   38 24572.1  646.63
plot (lm1)

Effect of pH and salinity on number of reproductive apices, interaction term removed

lm2 <- lm(apices.repro ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    302.6265       0.1607     -35.2942
summary (lm2)
## 
## Call:
## lm(formula = apices.repro ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.907 -19.912  -8.809  13.195  66.262 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 302.6265   233.3476   1.297    0.202
## salinity      0.1607     0.5334   0.301    0.765
## ph          -35.2942    29.3939  -1.201    0.237
## 
## Residual standard error: 25.13 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0395, Adjusted R-squared:  -0.009755 
## F-statistic: 0.8019 on 2 and 39 DF,  p-value: 0.4557
anova (lm2)
## Analysis of Variance Table
## 
## Response: apices.repro
##           Df  Sum Sq Mean Sq F value Pr(>F)
## salinity   1   102.4  102.41  0.1621 0.6894
## ph         1   910.7  910.67  1.4418 0.2371
## Residuals 39 24634.0  631.64
plot (lm2)

Effect and salinity and pH on number of reproductive apices: daily minimum salinity

lm3 <- lm(apices.repro ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = apices.repro ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##     304.88087        0.08016      -35.41148
summary (lm3)
## 
## Call:
## lm(formula = apices.repro ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.395 -19.858  -9.175  12.684  66.190 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)   304.88087  238.97854   1.276    0.210
## daily.min.sal   0.08016    0.51462   0.156    0.877
## ph            -35.41148   30.13906  -1.175    0.248
## 
## Residual standard error: 25.28 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.03889,    Adjusted R-squared:  -0.01451 
## F-statistic: 0.7283 on 2 and 36 DF,  p-value: 0.4897
anova (lm3)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.sal  1    48.6   48.61  0.0761 0.7843
## ph             1   882.3  882.33  1.3805 0.2477
## Residuals     36 23009.5  639.15
plot (lm3)

Effect and salinity and pH on number of reproductive apices: daily maximum salinity

lm4 <- lm(apices.repro ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = apices.repro ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      281.7583         0.3222       -33.3580
summary (lm4)
## 
## Call:
## lm(formula = apices.repro ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.572 -19.360  -8.928  11.295  67.906 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   281.7583   245.4662   1.148    0.259
## daily.max.sal   0.3222     0.7645   0.421    0.676
## ph            -33.3580    30.5165  -1.093    0.282
## 
## Residual standard error: 25.23 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.04296,    Adjusted R-squared:  -0.01021 
## F-statistic: 0.808 on 2 and 36 DF,  p-value: 0.4537
anova (lm4)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1   268.0  268.00  0.4211 0.5205
## ph             1   760.5  760.48  1.1949 0.2816
## Residuals     36 22911.9  636.44
plot (lm4)

Effect and salinity and pH on number of reproductive apices: daily salinity range

lm5 <- lm(apices.repro ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        302.1795           0.2909         -35.0801
summary (lm5)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.851 -18.494  -7.041  11.915  64.474 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)     302.1795   237.3132   1.273    0.211
## daily.sal.range   0.2909     0.8429   0.345    0.732
## ph              -35.0801    30.0365  -1.168    0.251
## 
## Residual standard error: 25.25 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.04141,    Adjusted R-squared:  -0.01185 
## F-statistic: 0.7776 on 2 and 36 DF,  p-value: 0.4671
anova (lm5)
## Analysis of Variance Table
## 
## Response: apices.repro
##                 Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range  1   121.8  121.81  0.1911 0.6646
## ph               1   869.5  869.53  1.3640 0.2505
## Residuals       36 22949.1  637.47
plot (lm5)

Effect and salinity and pH on number of reproductive apices: daily median salinity

lm6 <- lm(apices.repro ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = apices.repro ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      295.2991         0.1773       -34.5003
summary (lm6)
## 
## Call:
## lm(formula = apices.repro ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.869 -19.513  -8.856  12.322  67.313 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   295.2991   240.7094   1.227    0.228
## daily.med.sal   0.1773     0.5493   0.323    0.749
## ph            -34.5003    30.2576  -1.140    0.262
## 
## Residual standard error: 25.25 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.04101,    Adjusted R-squared:  -0.01226 
## F-statistic: 0.7698 on 2 and 36 DF,  p-value: 0.4706
anova (lm6)
## Analysis of Variance Table
## 
## Response: apices.repro
##               Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1   152.8  152.77  0.2396 0.6275
## ph             1   829.1  829.12  1.3001 0.2617
## Residuals     36 22958.5  637.74
plot (lm6)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 5

lm7 <- lm(apices.repro ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##         292.22152           -0.06613          -33.40128
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.766 -19.325  -8.706  13.810  64.334 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       292.22152  249.42459   1.172    0.248
## min.daily.sal.lt5  -0.06613    0.30872  -0.214    0.832
## ph                -33.40128   31.77793  -1.051    0.300
## 
## Residual standard error: 25.15 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0384, Adjusted R-squared:  -0.01091 
## F-statistic: 0.7787 on 2 and 39 DF,  p-value: 0.466
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                   Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt5  1   286.2  286.18  0.4526 0.5051
## ph                 1   698.6  698.63  1.1048 0.2997
## Residuals         39 24662.3  632.37
plot (lm7)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 10

lm8 <- lm(apices.repro ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##         312.573492            0.001009          -36.079126
summary (lm8)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.566 -19.468  -9.011  14.022  64.525 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)        312.573492 258.464271   1.209    0.234
## min.daily.sal.lt10   0.001009   0.325893   0.003    0.998
## ph                 -36.079126  33.021008  -1.093    0.281
## 
## Residual standard error: 25.16 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03727,    Adjusted R-squared:  -0.0121 
## F-statistic: 0.7548 on 2 and 39 DF,  p-value: 0.4768
anova (lm8)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1   200.0  199.99  0.3159 0.5773
## ph                  1   755.8  755.81  1.1938 0.2813
## Residuals          39 24691.3  633.11
plot (lm8)

Effect of salinity and pH on number of reproductive apices: number of days with a daily minimun salinity less than 15

lm9 <- lm(apices.repro ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##          337.36793             0.09779           -39.41691
summary (lm9)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.689 -18.258  -9.185  14.916  62.313 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        337.36793  244.02024   1.383    0.175
## min.daily.sal.lt15   0.09779    0.30431   0.321    0.750
## ph                 -39.41691   31.12358  -1.266    0.213
## 
## Residual standard error: 25.13 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03981,    Adjusted R-squared:  -0.009431 
## F-statistic: 0.8085 on 2 and 39 DF,  p-value: 0.4529
anova (lm9)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1     8.2    8.21  0.0130 0.9098
## ph                  1  1012.8 1012.79  1.6039 0.2129
## Residuals          39 24626.1  631.44
plot (lm9)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 5

lm10 <- lm(apices.repro ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##         302.78982           -0.02688          -34.79610
summary (lm10)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26.63 -19.71  -8.92  13.92  64.47 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)       302.78982  256.66031   1.180    0.245
## max.daily.sal.lt5  -0.02688    0.31657  -0.085    0.933
## ph                -34.79610   32.73641  -1.063    0.294
## 
## Residual standard error: 25.16 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03744,    Adjusted R-squared:  -0.01192 
## F-statistic: 0.7586 on 2 and 39 DF,  p-value: 0.4751
anova (lm10)
## Analysis of Variance Table
## 
## Response: apices.repro
##                   Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt5  1   245.2  245.20  0.3874 0.5373
## ph                 1   715.2  715.15  1.1298 0.2944
## Residuals         39 24686.8  632.99
plot (lm10)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 10

lm11 <- lm(apices.repro ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##          289.95871            -0.06402           -33.11102
summary (lm11)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.740 -19.409  -8.808  13.768  64.360 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        289.95871  256.45803   1.131    0.265
## max.daily.sal.lt10  -0.06402    0.31854  -0.201    0.842
## ph                 -33.11102   32.71606  -1.012    0.318
## 
## Residual standard error: 25.15 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03826,    Adjusted R-squared:  -0.01106 
## F-statistic: 0.7758 on 2 and 39 DF,  p-value: 0.4673
anova (lm11)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt10  1   333.5  333.52  0.5273 0.4721
## ph                  1   647.8  647.82  1.0243 0.3177
## Residuals          39 24665.8  632.46
plot (lm11)

Effect of salinity and pH on number of reproductive apices: number of days with a daily maximum salinity less than 15

lm12 <- lm(apices.repro ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           268.2051             -0.1223            -30.2510
summary (lm12)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.950 -18.681  -8.872  13.452  64.150 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        268.2051   259.0208   1.035    0.307
## max.daily.sal.lt15  -0.1223     0.3256  -0.376    0.709
## ph                 -30.2510    33.0708  -0.915    0.366
## 
## Residual standard error: 25.12 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04074,    Adjusted R-squared:  -0.008454 
## F-statistic: 0.8281 on 2 and 39 DF,  p-value: 0.4444
anova (lm12)
## Analysis of Variance Table
## 
## Response: apices.repro
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1   517.0  516.99  0.8195 0.3709
## ph                  1   527.8  527.84  0.8367 0.3660
## Residuals          39 24602.3  630.83
plot (lm12)

Effect of salinity and pH on number of reproductive apices: number of days with a daily salinity range greater than 10

lm13 <- lm(apices.repro ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##            324.63943               0.04107             -37.67656
summary (lm13)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.842 -19.068  -9.119  14.223  64.236 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)          324.63943  251.64516   1.290    0.205
## daily.sal.range.gt10   0.04107    0.32706   0.126    0.901
## ph                   -37.67656   32.11122  -1.173    0.248
## 
## Residual standard error: 25.16 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03766,    Adjusted R-squared:  -0.01169 
## F-statistic: 0.763 on 2 and 39 DF,  p-value: 0.4731
anova (lm13)
## Analysis of Variance Table
## 
## Response: apices.repro
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1    94.5   94.54  0.1494 0.7012
## ph                    1   871.2  871.23  1.3767 0.2478
## Residuals            39 24681.4  632.86
plot (lm13)

Effect of salinity and pH on number of reproductive apices: number of days with a daily salinity range greater than 5

lm14 <- lm(apices.repro ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##           323.07846              0.08621            -37.60134
summary (lm14)
## 
## Call:
## lm(formula = apices.repro ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.055 -18.376  -9.066  14.925  62.993 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)         323.07846  234.15060   1.380    0.176
## daily.sal.range.gt5   0.08621    0.29507   0.292    0.772
## ph                  -37.60134   29.78201  -1.263    0.214
## 
## Residual standard error: 25.13 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03937,    Adjusted R-squared:  -0.009894 
## F-statistic: 0.7992 on 2 and 39 DF,  p-value: 0.4569
anova (lm14)
## Analysis of Variance Table
## 
## Response: apices.repro
##                     Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1     2.7    2.71  0.0043 0.9481
## ph                   1  1007.0 1007.00  1.5940 0.2142
## Residuals           39 24637.4  631.73
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on number of reproductive apices: daily minimum ph

lm3 <- lm(apices.repro ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = apices.repro ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     331.3256      -39.4299        0.1521
summary (lm3)
## 
## Call:
## lm(formula = apices.repro ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.474 -18.299  -7.498  13.234  61.638 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  331.3256   280.8157   1.180    0.245
## daily.min.ph -39.4299    35.8585  -1.100    0.278
## salinity       0.1521     0.5361   0.284    0.778
## 
## Residual standard error: 25.21 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03394,    Adjusted R-squared:  -0.0156 
## F-statistic: 0.6852 on 2 and 39 DF,  p-value: 0.51
anova (lm3)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1   819.4  819.43  1.2898 0.2630
## salinity      1    51.1   51.12  0.0805 0.7782
## Residuals    39 24776.6  635.30
plot (lm3)

Effect salinity and pH on number of reproductive apices: daily maximum ph

lm4 <- lm(apices.repro ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = apices.repro ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##      98.2804       -9.3497        0.1899
summary (lm4)
## 
## Call:
## lm(formula = apices.repro ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.121 -18.912  -8.233  12.550  71.373 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)   98.2804   165.5398   0.594    0.556
## daily.max.ph  -9.3497    20.4605  -0.457    0.650
## salinity       0.1899     0.5425   0.350    0.728
## 
## Residual standard error: 25.52 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.009298,   Adjusted R-squared:  -0.04151 
## F-statistic: 0.183 on 2 and 39 DF,  p-value: 0.8335
anova (lm4)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1   158.6  158.58  0.2434 0.6245
## salinity      1    79.9   79.88  0.1226 0.7281
## Residuals    39 25408.7  651.50
plot (lm4)

Effect of salinity and pH on number of reproductive apices: daily ph range

lm5 <- lm(apices.repro ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        22.7620          0.5634          0.2097
summary (lm5)
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.670 -17.992  -8.053   8.548  73.528 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     22.7620    13.3110   1.710   0.0952 .
## daily.ph.range   0.5634     3.5430   0.159   0.8745  
## salinity         0.2097     0.5418   0.387   0.7008  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.004639,   Adjusted R-squared:  -0.04641 
## F-statistic: 0.09088 on 2 and 39 DF,  p-value: 0.9133
anova (lm5)
## Analysis of Variance Table
## 
## Response: apices.repro
##                Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1    20.9   20.93  0.0320 0.8590
## salinity        1    98.0   98.04  0.1498 0.7008
## Residuals      39 25528.2  654.57
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on number of reproductive apices: daily median ph

lm6 <- lm(apices.repro ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = apices.repro ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     206.0311      -23.1125        0.1857
summary (lm6)
## 
## Call:
## lm(formula = apices.repro ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.475 -18.344  -8.637  12.881  68.719 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  206.0311   231.0796   0.892    0.378
## daily.med.ph -23.1125    29.1130  -0.794    0.432
## salinity       0.1857     0.5382   0.345    0.732
## 
## Residual standard error: 25.39 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01983,    Adjusted R-squared:  -0.03043 
## F-statistic: 0.3946 on 2 and 39 DF,  p-value: 0.6766
anova (lm6)
## Analysis of Variance Table
## 
## Response: apices.repro
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1   431.9  431.91  0.6701 0.4180
## salinity      1    76.8   76.76  0.1191 0.7319
## Residuals    39 25138.5  644.58
plot (lm6)

Effect salinity and pH on number of reproductive apices: number of days with a daily minimun ph less than 7

lm7 <- lm(apices.repro ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##         27.89017          -0.58311           0.07788
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.418 -20.617  -4.852   8.224  70.938 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      27.89017   15.03132   1.855   0.0711 .
## min.daily.ph.lt7 -0.58311    0.83378  -0.699   0.4885  
## salinity          0.07788    0.57211   0.136   0.8924  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.43 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01633,    Adjusted R-squared:  -0.03412 
## F-statistic: 0.3237 on 2 and 39 DF,  p-value: 0.7254
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1   406.8  406.81  0.6289 0.4326
## salinity          1    12.0   11.99  0.0185 0.8924
## Residuals        39 25228.3  646.88
plot (lm7)

Effect salinity and pH on number of reproductive apices: number of days with a daily minimun ph less than 8

lm7 <- lm(apices.repro ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##         22.24009           0.02724           0.21487
summary (lm7)
## 
## Call:
## lm(formula = apices.repro ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.286 -18.223  -8.417   8.818  73.277 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      22.24009   14.25746   1.560    0.127
## min.daily.ph.lt8  0.02724    0.22014   0.124    0.902
## salinity          0.21487    0.54126   0.397    0.694
## 
## Residual standard error: 25.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.004384,   Adjusted R-squared:  -0.04667 
## F-statistic: 0.08586 on 2 and 39 DF,  p-value: 0.9179
anova (lm7)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1     9.3    9.25  0.0141 0.9060
## salinity          1   103.2  103.18  0.1576 0.6935
## Residuals        39 25534.7  654.74
plot (lm7)

Effect salinity and pH on number of reproductive apices: number of days with a daily maximum ph less than 7

lm10 <- lm(apices.repro ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = apices.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##         27.89017          -0.58311           0.07788
summary (lm10)
## 
## Call:
## lm(formula = apices.repro ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.418 -20.617  -4.852   8.224  70.938 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      27.89017   15.03132   1.855   0.0711 .
## max.daily.ph.lt7 -0.58311    0.83378  -0.699   0.4885  
## salinity          0.07788    0.57211   0.136   0.8924  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.43 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01633,    Adjusted R-squared:  -0.03412 
## F-statistic: 0.3237 on 2 and 39 DF,  p-value: 0.7254
anova (lm10)
## Analysis of Variance Table
## 
## Response: apices.repro
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1   406.8  406.81  0.6289 0.4326
## salinity          1    12.0   11.99  0.0185 0.8924
## Residuals        39 25228.3  646.88
plot (lm10)

Effect salinity and pH on number of reproductive apices: number of days with a daily ph range greater than 0.5

lm13 <- lm(apices.repro ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              27.9119               -0.4607                0.1088
summary (lm13)
## 
## Call:
## lm(formula = apices.repro ~ daily.ph.range.gt0.5 + salinity, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.748 -19.974  -6.129  12.522  70.835 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           27.9119    15.0476   1.855   0.0712 .
## daily.ph.range.gt0.5  -0.4607     0.6590  -0.699   0.4887  
## salinity               0.1088     0.5586   0.195   0.8465  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.43 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.01632,    Adjusted R-squared:  -0.03413 
## F-statistic: 0.3235 on 2 and 39 DF,  p-value: 0.7255
anova (lm13)
## Analysis of Variance Table
## 
## Response: apices.repro
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1   394.0  394.00  0.6091 0.4399
## salinity              1    24.6   24.56  0.0380 0.8465
## Residuals            39 25228.6  646.89
plot (lm13)

####Q2.4 Effects of salinity and pH on percent of reproductive apices#### Different salinity terms first

Effect of pH and salinity on percent reproductive apices

lm1 <- lm(perc.ra ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    -478.486       42.272       62.740       -5.311
summary (lm1)
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.412 -11.804  -4.422  11.214  36.928 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -478.486    640.424  -0.747    0.460
## salinity      42.272     28.618   1.477    0.148
## ph            62.740     80.959   0.775    0.443
## salinity:ph   -5.311      3.620  -1.467    0.151
## 
## Residual standard error: 16.53 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2194, Adjusted R-squared:  0.1578 
## F-statistic: 3.561 on 3 and 38 DF,  p-value: 0.02297
anova (lm1)
## Analysis of Variance Table
## 
## Response: perc.ra
##             Df  Sum Sq Mean Sq F value  Pr(>F)   
## salinity     1   308.9  308.89  1.1305 0.29439   
## ph           1  2022.2 2022.16  7.4006 0.00978 **
## salinity:ph  1   588.1  588.08  2.1522 0.15059   
## Residuals   38 10383.3  273.24                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on percent reproductive apices, interaction term removed

lm2 <- lm(perc.ra ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    433.6637       0.2922     -52.5933
summary (lm2)
## 
## Call:
## lm(formula = perc.ra ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.974 -13.814  -1.335  11.569  36.427 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 433.6637   155.7275   2.785  0.00822 **
## salinity      0.2922     0.3560   0.821  0.41679   
## ph          -52.5933    19.6164  -2.681  0.01070 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.77 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1752, Adjusted R-squared:  0.1329 
## F-statistic: 4.143 on 2 and 39 DF,  p-value: 0.02336
anova (lm2)
## Analysis of Variance Table
## 
## Response: perc.ra
##           Df  Sum Sq Mean Sq F value Pr(>F)  
## salinity   1   308.9  308.89  1.0980 0.3012  
## ph         1  2022.2 2022.16  7.1882 0.0107 *
## Residuals 39 10971.3  281.32                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on percent reproductive apices: daily minimum salinity

lm3 <- lm(perc.ra ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = perc.ra ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      441.4732         0.0805       -52.8976
summary (lm3)
## 
## Call:
## lm(formula = perc.ra ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.520 -12.654  -1.376  13.253  36.083 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   441.4732   164.1576   2.689   0.0108 *
## daily.min.sal   0.0805     0.3535   0.228   0.8211  
## ph            -52.8976    20.7029  -2.555   0.0150 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.37 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1582, Adjusted R-squared:  0.1115 
## F-statistic: 3.383 on 2 and 36 DF,  p-value: 0.04503
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1    71.9   71.86  0.2383 0.62841  
## ph             1  1968.9 1968.87  6.5284 0.01499 *
## Residuals     36 10857.0  301.58                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on percent reproductive apices: daily maximum salinity

lm4 <- lm(perc.ra ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = perc.ra ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      369.0251         0.8799       -46.4712
summary (lm4)
## 
## Call:
## lm(formula = perc.ra ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.904 -12.659  -1.552  10.754  33.201 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   369.0251   162.4056   2.272   0.0291 *
## daily.max.sal   0.8799     0.5058   1.740   0.0905 .
## ph            -46.4712    20.1904  -2.302   0.0272 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.69 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2224, Adjusted R-squared:  0.1792 
## F-statistic: 5.148 on 2 and 36 DF,  p-value: 0.01081
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.sal  1  1392.3  1392.3  4.9976 0.03167 *
## ph             1  1475.9  1475.9  5.2976 0.02725 *
## Residuals     36 10029.5   278.6                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on percent reproductive apices: daily salinity range

lm5 <- lm(perc.ra ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        421.3228           0.9192         -50.8287
summary (lm5)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -30.008 -14.543  -2.331  12.162  29.795 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     421.3228   157.5481   2.674   0.0112 *
## daily.sal.range   0.9192     0.5596   1.643   0.1092  
## ph              -50.8287    19.9407  -2.549   0.0152 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.76 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2158, Adjusted R-squared:  0.1722 
## F-statistic: 4.953 on 2 and 36 DF,  p-value: 0.01258
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.ra
##                 Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1   957.7  957.68  3.4086 0.07309 .
## ph               1  1825.5 1825.50  6.4974 0.01521 *
## Residuals       36 10114.6  280.96                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent reproductive apices: daily median salinity

lm6 <- lm(perc.ra ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = perc.ra ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      417.6358         0.3465       -50.6672
summary (lm6)
## 
## Call:
## lm(formula = perc.ra ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.135 -13.956   0.236  11.448  35.971 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   417.6358   163.7048   2.551   0.0151 *
## daily.med.sal   0.3465     0.3736   0.927   0.3599  
## ph            -50.6672    20.5780  -2.462   0.0187 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.17 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1767, Adjusted R-squared:  0.1309 
## F-statistic: 3.863 on 2 and 36 DF,  p-value: 0.03021
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.ra
##               Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1   490.6  490.59  1.6632 0.20540  
## ph             1  1788.2 1788.24  6.0624 0.01873 *
## Residuals     36 10618.9  294.97                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 5

lm7 <- lm(perc.ra ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          323.5167            -0.4219           -37.1483
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.184 -12.228  -2.209  10.646  30.847 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       323.5167   158.6635   2.039   0.0483 *
## min.daily.sal.lt5  -0.4219     0.1964  -2.149   0.0379 *
## ph                -37.1483    20.2145  -1.838   0.0737 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2498, Adjusted R-squared:  0.2113 
## F-statistic: 6.493 on 2 and 39 DF,  p-value: 0.003681
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                   Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt5  1 2458.7 2458.71  9.6086 0.003587 **
## ph                 1  864.2  864.16  3.3772 0.073733 . 
## Residuals         39 9979.5  255.88                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 10

lm8 <- lm(perc.ra ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           291.1214             -0.4531            -32.8332
summary (lm8)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.053 -12.515  -2.228  10.561  29.551 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        291.1214   163.9681   1.775   0.0836 .
## min.daily.sal.lt10  -0.4531     0.2067  -2.191   0.0345 *
## ph                 -32.8332    20.9483  -1.567   0.1251  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.253,  Adjusted R-squared:  0.2147 
## F-statistic: 6.604 on 2 and 39 DF,  p-value: 0.003388
anova (lm8)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt10  1 2739.3 2739.30 10.7508 0.002198 **
## ph                  1  625.9  625.93  2.4566 0.125113   
## Residuals          39 9937.2  254.80                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on percent reproductive apices: number of days with a daily minimun salinity less than 15

lm9 <- lm(perc.ra ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##             340.52               -0.43              -39.05
summary (lm9)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.801 -11.066  -2.693   9.769  33.113 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        340.5213   154.7222   2.201   0.0337 *
## min.daily.sal.lt15  -0.4300     0.1929  -2.228   0.0317 *
## ph                 -39.0529    19.7340  -1.979   0.0549 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.93 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2557, Adjusted R-squared:  0.2176 
## F-statistic: 6.701 on 2 and 39 DF,  p-value: 0.003152
anova (lm9)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt15  1 2407.9 2407.89  9.4853 0.003785 **
## ph                  1  994.2  994.17  3.9163 0.054912 . 
## Residuals          39 9900.3  253.85                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 5

lm10 <- lm(perc.ra ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          333.0821            -0.3366           -38.4611
summary (lm10)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.148 -11.956  -0.988  10.259  31.930 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       333.0821   166.9504   1.995   0.0531 .
## max.daily.sal.lt5  -0.3366     0.2059  -1.634   0.1102  
## ph                -38.4611    21.2941  -1.806   0.0786 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.37 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2148, Adjusted R-squared:  0.1745 
## F-statistic: 5.334 on 2 and 39 DF,  p-value: 0.008961
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.ra
##                   Df  Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt5  1  1983.3 1983.33  7.4052 0.009668 **
## ph                 1   873.7  873.74  3.2623 0.078611 . 
## Residuals         39 10445.3  267.83                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 10

lm11 <- lm(perc.ra ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           320.6320             -0.3753            -36.8125
summary (lm11)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -27.353 -12.031  -1.177  10.414  31.401 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        320.6320   165.5879   1.936   0.0601 .
## max.daily.sal.lt10  -0.3753     0.2057  -1.825   0.0757 .
## ph                 -36.8125    21.1239  -1.743   0.0893 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.24 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.227,  Adjusted R-squared:  0.1873 
## F-statistic: 5.726 on 2 and 39 DF,  p-value: 0.006602
anova (lm11)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df  Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt10  1  2218.6 2218.64  8.4146 0.006089 **
## ph                  1   800.8  800.76  3.0370 0.089267 . 
## Residuals          39 10283.0  263.67                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on percent reproductive apices: number of days with a daily maximum salinity less than 15

lm12 <- lm(perc.ra ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           286.7255             -0.4569            -32.3435
summary (lm12)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.406 -13.088  -1.584   9.886  30.076 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        286.7255   164.4659   1.743   0.0891 .
## max.daily.sal.lt15  -0.4569     0.2068  -2.210   0.0330 *
## ph                 -32.3435    20.9984  -1.540   0.1316  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.95 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2544, Adjusted R-squared:  0.2161 
## F-statistic: 6.652 on 2 and 39 DF,  p-value: 0.003268
anova (lm12)
## Analysis of Variance Table
## 
## Response: perc.ra
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt15  1 2780.2 2780.22 10.9317 0.002036 **
## ph                  1  603.4  603.39  2.3725 0.131566   
## Residuals          39 9918.8  254.33                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on percent reproductive apices: number of days with a daily salinity range greater than 10

lm13 <- lm(perc.ra ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             330.0779               -0.4001              -37.9139
summary (lm13)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -25.814 -11.445  -1.621  10.476  30.728 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          330.0779   161.8796   2.039   0.0483 *
## daily.sal.range.gt10  -0.4001     0.2104  -1.902   0.0646 .
## ph                   -37.9139    20.6567  -1.835   0.0741 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.18 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2322, Adjusted R-squared:  0.1928 
## F-statistic: 5.897 on 2 and 39 DF,  p-value: 0.005785
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.ra
##                      Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt10  1  2206.6 2206.61  8.4259 0.006058 **
## ph                    1   882.2  882.24  3.3688 0.074076 . 
## Residuals            39 10213.5  261.89                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on percent reproductive apices: number of days with a daily salinity range greater than 5

lm14 <- lm(perc.ra ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            388.0782              -0.5002             -44.8293
summary (lm14)
## 
## Call:
## lm(formula = perc.ra ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.441 -11.295  -1.957  11.923  32.284 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         388.0782   144.2083   2.691  0.01043 * 
## daily.sal.range.gt5  -0.5002     0.1817  -2.753  0.00892 **
## ph                  -44.8293    18.3421  -2.444  0.01915 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.48 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2975, Adjusted R-squared:  0.2615 
## F-statistic: 8.257 on 2 and 39 DF,  p-value: 0.001023
anova (lm14)
## Analysis of Variance Table
## 
## Response: perc.ra
##                     Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt5  1 2525.9 2525.91 10.5414 0.002402 **
## ph                   1 1431.4 1431.35  5.9735 0.019148 * 
## Residuals           39 9345.1  239.62                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent reproductive apices: daily minimum ph

lm3 <- lm(perc.ra ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = perc.ra ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##      334.246       -40.580         0.308
summary (lm3)
## 
## Call:
## lm(formula = perc.ra ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25.10 -13.45  -3.59  13.35  38.34 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  334.2456   196.8893   1.698   0.0975 .
## daily.min.ph -40.5802    25.1416  -1.614   0.1146  
## salinity       0.3080     0.3759   0.819   0.4176  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.67 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08438,    Adjusted R-squared:  0.03743 
## F-statistic: 1.797 on 2 and 39 DF,  p-value: 0.1792
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1   912.9  912.86  2.9230 0.09527 .
## salinity      1   209.6  209.64  0.6713 0.41759  
## Residuals    39 12179.9  312.30                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on percent reproductive apices: daily maximum ph

lm4 <- lm(perc.ra ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = perc.ra ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     307.3058      -36.0220        0.2789
summary (lm4)
## 
## Call:
## lm(formula = perc.ra ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.474 -13.973   0.021   9.756  33.263 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  307.3058   108.7918   2.825  0.00742 **
## daily.max.ph -36.0220    13.4465  -2.679  0.01076 * 
## salinity       0.2789     0.3565   0.782  0.43882   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.77 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.175,  Adjusted R-squared:  0.1327 
## F-statistic: 4.137 on 2 and 39 DF,  p-value: 0.02347
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.ph  1  2156.1 2156.11  7.6624 0.008581 **
## salinity      1   172.2  172.16  0.6118 0.438821   
## Residuals    39 10974.1  281.39                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on percent reproductive apices: daily ph range

lm5 <- lm(perc.ra ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##        15.8531          4.5974          0.3362
summary (lm5)
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.779 -12.518  -4.952  11.551  37.380 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     15.8531     9.0848   1.745   0.0889 .
## daily.ph.range   4.5974     2.4181   1.901   0.0647 .
## salinity         0.3362     0.3698   0.909   0.3688  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.46 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1061, Adjusted R-squared:  0.06023 
## F-statistic: 2.314 on 2 and 39 DF,  p-value: 0.1123
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.ra
##                Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.ph.range  1   1159 1159.00  3.8012 0.05843 .
## salinity        1    252  252.05  0.8266 0.36883  
## Residuals      39  11891  304.91                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on percent reproductive apices: daily median ph

lm6 <- lm(perc.ra ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = perc.ra ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     408.1510      -49.3856        0.3112
summary (lm6)
## 
## Call:
## lm(formula = perc.ra ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.700 -14.398  -1.327  11.557  36.133 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  408.1510   153.8186   2.653   0.0115 *
## daily.med.ph -49.3856    19.3791  -2.548   0.0149 *
## salinity       0.3112     0.3582   0.869   0.3903  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.9 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1627, Adjusted R-squared:  0.1197 
## F-statistic: 3.788 on 2 and 39 DF,  p-value: 0.03138
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.ra
##              Df  Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.ph  1  1948.2 1948.20  6.8212 0.01272 *
## salinity      1   215.5  215.52  0.7546 0.39034  
## Residuals    39 11138.7  285.61                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on percent reproductive apices: number of days with a daily minimun ph less than 7

lm7 <- lm(perc.ra ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          15.6988            0.1280            0.4016
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23.45 -12.73  -6.12  12.23  37.90 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       15.6988    10.7810   1.456    0.153
## min.daily.ph.lt7   0.1280     0.5980   0.214    0.832
## salinity           0.4016     0.4103   0.979    0.334
## 
## Residual standard error: 18.24 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02437,    Adjusted R-squared:  -0.02567 
## F-statistic: 0.487 on 2 and 39 DF,  p-value: 0.6181
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1     5.3    5.33   0.016 0.8999
## salinity          1   318.8  318.81   0.958 0.3337
## Residuals        39 12978.2  332.78
plot (lm7)

Effect salinity and pH on percent reproductive apices: number of days with a daily minimun ph less than 8

lm7 <- lm(perc.ra ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          24.7338           -0.3388            0.3616
summary (lm7)
## 
## Call:
## lm(formula = perc.ra ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.807 -12.724  -3.747   7.644  40.377 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       24.7338     9.5442   2.592   0.0134 *
## min.daily.ph.lt8  -0.3388     0.1474  -2.299   0.0269 *
## salinity           0.3616     0.3623   0.998   0.3245  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17.13 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1398, Adjusted R-squared:  0.09569 
## F-statistic: 3.169 on 2 and 39 DF,  p-value: 0.05305
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt8  1  1567.6 1567.57  5.3427 0.02618 *
## salinity          1   292.1  292.15  0.9957 0.32450  
## Residuals        39 11442.7  293.40                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on percent reproductive apices: number of days with a daily maximum ph less than 7

lm10 <- lm(perc.ra ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = perc.ra ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          15.6988            0.1280            0.4016
summary (lm10)
## 
## Call:
## lm(formula = perc.ra ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -23.45 -12.73  -6.12  12.23  37.90 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       15.6988    10.7810   1.456    0.153
## max.daily.ph.lt7   0.1280     0.5980   0.214    0.832
## salinity           0.4016     0.4103   0.979    0.334
## 
## Residual standard error: 18.24 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.02437,    Adjusted R-squared:  -0.02567 
## F-statistic: 0.487 on 2 and 39 DF,  p-value: 0.6181
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.ra
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1     5.3    5.33   0.016 0.8999
## salinity          1   318.8  318.81   0.958 0.3337
## Residuals        39 12978.2  332.78
plot (lm10)

Effect salinity and pH on percent reproductive apices: number of days with a daily ph range greater than 0.5

lm13 <- lm(perc.ra ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              22.0641               -0.4818                0.2617
summary (lm13)
## 
## Call:
## lm(formula = perc.ra ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.854 -15.196  -3.051  10.686  34.561 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           22.0641    10.6543   2.071    0.045 *
## daily.ph.range.gt0.5  -0.4818     0.4666  -1.033    0.308  
## salinity               0.2617     0.3955   0.662    0.512  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18.01 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04922,    Adjusted R-squared:  0.0004577 
## F-statistic: 1.009 on 2 and 39 DF,  p-value: 0.3738
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.ra
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1   512.7  512.71  1.5810 0.2161
## salinity              1   142.0  141.98  0.4378 0.5121
## Residuals            39 12647.7  324.30
plot (lm13)

####Q2.5 Effects of salinity and pH on number of oogonia#### Different salinity terms first

Effect of pH and salinity on number of oogonia

lm1 <- lm(avg.oog ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##     -80.464       14.797       11.439       -1.815
summary (lm1)
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.493 -13.627  -2.085  12.392  36.563 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -80.464    640.003  -0.126    0.901
## salinity      14.797     28.599   0.517    0.608
## ph            11.439     80.906   0.141    0.888
## salinity:ph   -1.815      3.618  -0.502    0.619
## 
## Residual standard error: 16.52 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1032, Adjusted R-squared:  0.0324 
## F-statistic: 1.458 on 3 and 38 DF,  p-value: 0.2414
anova (lm1)
## Analysis of Variance Table
## 
## Response: avg.oog
##             Df  Sum Sq Mean Sq F value Pr(>F)
## salinity     1   553.1  553.06  2.0267 0.1627
## ph           1   571.6  571.63  2.0948 0.1560
## salinity:ph  1    68.6   68.64  0.2515 0.6189
## Residuals   38 10369.6  272.88
plot (lm1)

Effect of pH and salinity on number of oogonia, interaction term removed

lm2 <- lm(avg.oog ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    231.1591       0.4551     -27.9628
summary (lm2)
## 
## Call:
## lm(formula = avg.oog ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.261 -13.201  -2.266  13.031  37.308 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 231.1591   151.8971   1.522    0.136
## salinity      0.4551     0.3472   1.311    0.198
## ph          -27.9628    19.1339  -1.461    0.152
## 
## Residual standard error: 16.36 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09727,    Adjusted R-squared:  0.05097 
## F-statistic: 2.101 on 2 and 39 DF,  p-value: 0.136
anova (lm2)
## Analysis of Variance Table
## 
## Response: avg.oog
##           Df  Sum Sq Mean Sq F value Pr(>F)
## salinity   1   553.1  553.06  2.0664 0.1586
## ph         1   571.6  571.63  2.1358 0.1519
## Residuals 39 10438.3  267.65
plot (lm2)

Effect and salinity and pH on number of oogonia: daily minimum salinity

lm3 <- lm(avg.oog ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = avg.oog ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      262.9507         0.3537       -31.6026
summary (lm3)
## 
## Call:
## lm(formula = avg.oog ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.935 -13.485  -0.503  11.784  38.379 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   262.9507   154.1312   1.706   0.0966 .
## daily.min.sal   0.3537     0.3319   1.066   0.2937  
## ph            -31.6026    19.4384  -1.626   0.1127  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.31 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1038, Adjusted R-squared:  0.05405 
## F-statistic: 2.086 on 2 and 36 DF,  p-value: 0.139
anova (lm3)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.min.sal  1  406.3  406.25  1.5280 0.2244
## ph             1  702.7  702.73  2.6432 0.1127
## Residuals     36 9571.3  265.87
plot (lm3)

Effect and salinity and pH on number of oogonia: daily maximum salinity

lm4 <- lm(avg.oog ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = avg.oog ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      246.2015         0.4579       -30.1017
summary (lm4)
## 
## Call:
## lm(formula = avg.oog ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.825 -13.637   0.248  12.722  38.195 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   246.2015   159.2610   1.546    0.131
## daily.max.sal   0.4579     0.4960   0.923    0.362
## ph            -30.1017    19.7994  -1.520    0.137
## 
## Residual standard error: 16.37 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.09694,    Adjusted R-squared:  0.04677 
## F-statistic: 1.932 on 2 and 36 DF,  p-value: 0.1595
anova (lm4)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.max.sal  1  416.1  416.11  1.5532 0.2207
## ph             1  619.3  619.26  2.3114 0.1372
## Residuals     36 9644.9  267.91
plot (lm4)

Effect and salinity and pH on number of oogonia: daily salinity range

lm5 <- lm(avg.oog ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##        297.2953          -0.3804         -34.7467
summary (lm5)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.466 -13.697  -1.289   9.071  39.979 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     297.2953   154.6305   1.923   0.0625 .
## daily.sal.range  -0.3804     0.5492  -0.693   0.4930  
## ph              -34.7467    19.5714  -1.775   0.0843 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.45 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.08772,    Adjusted R-squared:  0.03703 
## F-statistic: 1.731 on 2 and 36 DF,  p-value: 0.1916
anova (lm5)
## Analysis of Variance Table
## 
## Response: avg.oog
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1   83.8   83.75  0.3094 0.58146  
## ph               1  853.1  853.08  3.1520 0.08429 .
## Residuals       36 9743.4  270.65                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on number of oogonia: daily median salinity

lm6 <- lm(avg.oog ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = avg.oog ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##       252.613          0.404        -30.538
summary (lm6)
## 
## Call:
## lm(formula = avg.oog ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.492 -13.263  -0.707  12.540  37.566 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   252.6126   155.0693   1.629    0.112
## daily.med.sal   0.4040     0.3538   1.142    0.261
## ph            -30.5377    19.4925  -1.567    0.126
## 
## Residual standard error: 16.27 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1079, Adjusted R-squared:  0.05831 
## F-statistic: 2.176 on 2 and 36 DF,  p-value: 0.1281
anova (lm6)
## Analysis of Variance Table
## 
## Response: avg.oog
##               Df Sum Sq Mean Sq F value Pr(>F)
## daily.med.sal  1  502.5  502.49  1.8985 0.1767
## ph             1  649.6  649.60  2.4544 0.1259
## Residuals     36 9528.2  264.67
plot (lm6)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 5

lm7 <- lm(avg.oog ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          299.0783             0.1348           -35.4148
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.852 -12.309  -3.513   8.763  41.217 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       299.0783   164.8852   1.814   0.0774 .
## min.daily.sal.lt5   0.1348     0.2041   0.660   0.5129  
## ph                -35.4148    21.0072  -1.686   0.0998 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.62 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06793,    Adjusted R-squared:  0.02013 
## F-statistic: 1.421 on 2 and 39 DF,  p-value: 0.2537
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                   Df  Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.sal.lt5  1     0.0    0.03  0.0001 0.99245  
## ph                 1   785.4  785.39  2.8421 0.09981 .
## Residuals         39 10777.5  276.35                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 10

lm8 <- lm(avg.oog ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##          274.30490             0.04525           -32.16061
summary (lm8)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.499 -12.556  -3.840   8.498  40.567 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        274.30490  171.61644   1.598    0.118
## min.daily.sal.lt10   0.04525    0.21639   0.209    0.835
## ph                 -32.16061   21.92546  -1.467    0.150
## 
## Residual standard error: 16.71 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.05856,    Adjusted R-squared:  0.01028 
## F-statistic: 1.213 on 2 and 39 DF,  p-value: 0.3083
anova (lm8)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt10  1    76.6   76.57  0.2743 0.6034
## ph                  1   600.5  600.55  2.1515 0.1504
## Residuals          39 10885.8  279.12
plot (lm8)

Effect of salinity and pH on number of oogonia: number of days with a daily minimun salinity less than 15

lm9 <- lm(avg.oog ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           237.3401             -0.0816            -27.2287
summary (lm9)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.401 -13.844  -3.042   9.422  38.992 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        237.3401   161.9926   1.465    0.151
## min.daily.sal.lt15  -0.0816     0.2020  -0.404    0.688
## ph                 -27.2287    20.6614  -1.318    0.195
## 
## Residual standard error: 16.68 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06143,    Adjusted R-squared:  0.0133 
## F-statistic: 1.276 on 2 and 39 DF,  p-value: 0.2905
anova (lm9)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.sal.lt15  1   227.0  227.02  0.8158 0.3719
## ph                  1   483.3  483.29  1.7367 0.1952
## Residuals          39 10852.6  278.27
plot (lm9)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 5

lm10 <- lm(avg.oog ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          316.4683             0.1658           -37.6761
summary (lm10)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.862 -12.885  -3.534   8.911  41.524 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       316.4683   169.1659   1.871   0.0689 .
## max.daily.sal.lt5   0.1658     0.2087   0.795   0.4316  
## ph                -37.6761    21.5767  -1.746   0.0887 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07252,    Adjusted R-squared:  0.02496 
## F-statistic: 1.525 on 2 and 39 DF,  p-value: 0.2304
anova (lm10)
## Analysis of Variance Table
## 
## Response: avg.oog
##                   Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt5  1     0.1    0.11  0.0004 0.98380  
## ph                 1   838.4  838.43  3.0490 0.08866 .
## Residuals         39 10724.4  274.98                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 10

lm11 <- lm(avg.oog ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           314.4028              0.1613            -37.4123
summary (lm11)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.823 -12.788  -4.155   8.948  41.526 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        314.4028   169.1950   1.858   0.0707 .
## max.daily.sal.lt10   0.1613     0.2102   0.767   0.4474  
## ph                 -37.4123    21.5840  -1.733   0.0909 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07153,    Adjusted R-squared:  0.02391 
## F-statistic: 1.502 on 2 and 39 DF,  p-value: 0.2352
anova (lm11)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value  Pr(>F)  
## max.daily.sal.lt10  1     0.0    0.00  0.0000 0.99776  
## ph                  1   827.1  827.06  3.0044 0.09094 .
## Residuals          39 10735.9  275.28                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on number of oogonia: number of days with a daily maximum salinity less than 15

lm12 <- lm(avg.oog ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##            309.778               0.143             -36.811
summary (lm12)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.807 -12.427  -3.898   8.647  41.458 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        309.7778   171.4283   1.807   0.0785 .
## max.daily.sal.lt15   0.1430     0.2155   0.664   0.5108  
## ph                 -36.8113    21.8873  -1.682   0.1006  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.62 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06803,    Adjusted R-squared:  0.02023 
## F-statistic: 1.423 on 2 and 39 DF,  p-value: 0.2531
anova (lm12)
## Analysis of Variance Table
## 
## Response: avg.oog
##                    Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.sal.lt15  1     5.0    4.99  0.0181 0.8938
## ph                  1   781.6  781.60  2.8286 0.1006
## Residuals          39 10776.3  276.32
plot (lm12)

Effect of salinity and pH on number of oogonia: number of days with a daily salinity range greater than 10

lm13 <- lm(avg.oog ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##            285.69779               0.09049             -33.67639
summary (lm13)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -24.899 -12.612  -3.594   8.275  41.012 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          285.69779  166.84396   1.712   0.0948 .
## daily.sal.range.gt10   0.09049    0.21684   0.417   0.6787  
## ph                   -33.67639   21.29015  -1.582   0.1218  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.68 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06169,    Adjusted R-squared:  0.01358 
## F-statistic: 1.282 on 2 and 39 DF,  p-value: 0.2889
anova (lm13)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt10  1    17.3   17.31  0.0622 0.8043
## ph                    1   696.1  696.05  2.5020 0.1218
## Residuals            39 10849.6  278.19
plot (lm13)

Effect of salinity and pH on number of oogonia: number of days with a daily salinity range greater than 5

lm14 <- lm(avg.oog ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##             242.321               -0.127              -27.741
summary (lm14)
## 
## Call:
## lm(formula = avg.oog ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.986 -14.151  -3.420   9.275  38.801 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         242.3206   154.8908   1.564    0.126
## daily.sal.range.gt5  -0.1270     0.1952  -0.651    0.519
## ph                  -27.7405    19.7008  -1.408    0.167
## 
## Residual standard error: 16.63 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06763,    Adjusted R-squared:  0.01982 
## F-statistic: 1.414 on 2 and 39 DF,  p-value: 0.2552
anova (lm14)
## Analysis of Variance Table
## 
## Response: avg.oog
##                     Df  Sum Sq Mean Sq F value Pr(>F)
## daily.sal.range.gt5  1   233.9  233.93  0.8462 0.3633
## ph                   1   548.1  548.09  1.9827 0.1670
## Residuals           39 10780.9  276.43
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on number of oogonia: daily minimum ph

lm3 <- lm(avg.oog ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = avg.oog ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     -177.312        23.883         0.535
summary (lm3)
## 
## Call:
## lm(formula = avg.oog ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.923 -12.938  -1.797   8.245  37.646 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -177.3120   184.7826  -0.960    0.343
## daily.min.ph   23.8834    23.5957   1.012    0.318
## salinity        0.5350     0.3528   1.516    0.137
## 
## Residual standard error: 16.59 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0722, Adjusted R-squared:  0.02462 
## F-statistic: 1.518 on 2 and 39 DF,  p-value: 0.2319
anova (lm3)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.min.ph  1   202.3  202.27  0.7353 0.3964
## salinity      1   632.6  632.61  2.2997 0.1375
## Residuals    39 10728.1  275.08
plot (lm3)

Effect salinity and pH on number of oogonia: daily maximum ph

lm4 <- lm(avg.oog ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = avg.oog ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     162.1239      -18.9223        0.4486
summary (lm4)
## 
## Call:
## lm(formula = avg.oog ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.008 -13.314  -3.066  13.262  36.416 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  162.1239   106.1756   1.527    0.135
## daily.max.ph -18.9223    13.1232  -1.442    0.157
## salinity       0.4486     0.3479   1.289    0.205
## 
## Residual standard error: 16.37 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09602,    Adjusted R-squared:  0.04966 
## F-statistic: 2.071 on 2 and 39 DF,  p-value: 0.1397
anova (lm4)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.max.ph  1   664.7  664.67  2.4799 0.1234
## salinity      1   445.6  445.62  1.6626 0.2048
## Residuals    39 10452.7  268.02
plot (lm4)

Effect of salinity and pH on number of oogonia: daily ph range

lm5 <- lm(avg.oog ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##         9.9197         -1.9376          0.5124
summary (lm5)
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.283 -14.154  -1.249   9.823  37.752 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      9.9197     8.6635   1.145    0.259
## daily.ph.range  -1.9376     2.3060  -0.840    0.406
## salinity         0.5124     0.3527   1.453    0.154
## 
## Residual standard error: 16.65 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.06476,    Adjusted R-squared:  0.0168 
## F-statistic:  1.35 on 2 and 39 DF,  p-value: 0.271
anova (lm5)
## Analysis of Variance Table
## 
## Response: avg.oog
##                Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1   163.5  163.48  0.5896 0.4472
## salinity        1   585.3  585.34  2.1110 0.1542
## Residuals      39 10814.1  277.28
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on number of oogonia: daily median ph

lm6 <- lm(avg.oog ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = avg.oog ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     198.8468      -23.8915        0.4681
summary (lm6)
## 
## Call:
## lm(formula = avg.oog ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.561 -12.381  -2.534  12.654  37.076 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)  198.8468   149.8817   1.327    0.192
## daily.med.ph -23.8915    18.8831  -1.265    0.213
## salinity       0.4681     0.3491   1.341    0.188
## 
## Residual standard error: 16.47 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08537,    Adjusted R-squared:  0.03847 
## F-statistic:  1.82 on 2 and 39 DF,  p-value: 0.1755
anova (lm6)
## Analysis of Variance Table
## 
## Response: avg.oog
##              Df  Sum Sq Mean Sq F value Pr(>F)
## daily.med.ph  1   499.5  499.47  1.8419 0.1825
## salinity      1   487.7  487.69  1.7984 0.1877
## Residuals    39 10575.8  271.17
plot (lm6)

Effect salinity and pH on number of oogonia: number of days with a daily minimun ph less than 7

lm7 <- lm(avg.oog ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##           4.7456            0.5555            0.6272
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.108 -13.143  -4.687   9.081  38.820 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)        4.7456     9.7995   0.484    0.631
## min.daily.ph.lt7   0.5555     0.5436   1.022    0.313
## salinity           0.6272     0.3730   1.681    0.101
## 
## Residual standard error: 16.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07267,    Adjusted R-squared:  0.02511 
## F-statistic: 1.528 on 2 and 39 DF,  p-value: 0.2297
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1    62.9   62.90  0.2288 0.6351
## salinity          1   777.3  777.33  2.8273 0.1007
## Residuals        39 10722.7  274.94
plot (lm7)

Effect salinity and pH on number of oogonia: number of days with a daily minimun ph less than 8

lm7 <- lm(avg.oog ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##         9.407150          0.004857          0.497566
summary (lm7)
## 
## Call:
## lm(formula = avg.oog ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.173 -14.229  -2.328  10.337  38.564 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)      9.407150   9.361852   1.005    0.321
## min.daily.ph.lt8 0.004857   0.144549   0.034    0.973
## salinity         0.497566   0.355405   1.400    0.169
## 
## Residual standard error: 16.8 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.04786,    Adjusted R-squared:  -0.0009703 
## F-statistic: 0.9801 on 2 and 39 DF,  p-value: 0.3843
anova (lm7)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt8  1     0.1    0.08  0.0003 0.9869
## salinity          1   553.3  553.30  1.9600 0.1694
## Residuals        39 11009.6  282.30
plot (lm7)

Effect salinity and pH on number of oogonia: number of days with a daily maximum ph less than 7

lm10 <- lm(avg.oog ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = avg.oog ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##           4.7456            0.5555            0.6272
summary (lm10)
## 
## Call:
## lm(formula = avg.oog ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.108 -13.143  -4.687   9.081  38.820 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)        4.7456     9.7995   0.484    0.631
## max.daily.ph.lt7   0.5555     0.5436   1.022    0.313
## salinity           0.6272     0.3730   1.681    0.101
## 
## Residual standard error: 16.58 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07267,    Adjusted R-squared:  0.02511 
## F-statistic: 1.528 on 2 and 39 DF,  p-value: 0.2297
anova (lm10)
## Analysis of Variance Table
## 
## Response: avg.oog
##                  Df  Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1    62.9   62.90  0.2288 0.6351
## salinity          1   777.3  777.33  2.8273 0.1007
## Residuals        39 10722.7  274.94
plot (lm10)

Effect salinity and pH on number of oogonia: number of days with a daily ph range greater than 0.5

lm13 <- lm(avg.oog ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              12.4835               -0.2711                0.4355
summary (lm13)
## 
## Call:
## lm(formula = avg.oog ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.176 -13.484  -3.029   9.247  37.610 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           12.4835     9.8910   1.262    0.214
## daily.ph.range.gt0.5  -0.2711     0.4332  -0.626    0.535
## salinity               0.4355     0.3672   1.186    0.243
## 
## Residual standard error: 16.72 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.0573, Adjusted R-squared:  0.008956 
## F-statistic: 1.185 on 2 and 39 DF,  p-value: 0.3164
anova (lm13)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1   269.4  269.39  0.9638 0.3323
## salinity              1   393.2  393.17  1.4067 0.2428
## Residuals            39 10900.4  279.50
plot (lm13)

####Q2.6 Effects of salinity and pH on percent reproductive dry weight####

Different salinity terms first

Effect of pH and salinity on percent reproductive dry weight

lm1 <- lm(perc.rdw ~ salinity + ph + salinity:ph, data =all)  
lm1
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph + salinity:ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  salinity:ph  
##    -448.732       36.384       58.083       -4.568
summary (lm1)
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph + salinity:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.236  -9.319  -2.697   6.940  32.511 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -448.732    494.558  -0.907    0.370
## salinity      36.384     22.100   1.646    0.108
## ph            58.083     62.519   0.929    0.359
## salinity:ph   -4.568      2.796  -1.634    0.111
## 
## Residual standard error: 12.77 on 38 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2373, Adjusted R-squared:  0.1771 
## F-statistic: 3.941 on 3 and 38 DF,  p-value: 0.01528
anova (lm1)
## Analysis of Variance Table
## 
## Response: perc.rdw
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## salinity     1  255.6  255.57  1.5684 0.218092   
## ph           1 1236.0 1236.03  7.5854 0.008979 **
## salinity:ph  1  435.1  435.07  2.6700 0.110516   
## Residuals   38 6192.0  162.95                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm1)

Effect of pH and salinity on percent reproductive dry weight, interaction term removed

lm2 <- lm(perc.rdw ~ salinity + ph, data =all)  
lm2
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph, data = all)
## 
## Coefficients:
## (Intercept)     salinity           ph  
##    335.8308       0.2759     -41.1184
summary (lm2)
## 
## Call:
## lm(formula = perc.rdw ~ salinity + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.682 -10.710  -1.672   8.938  29.576 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 335.8308   121.0312   2.775  0.00844 **
## salinity      0.2759     0.2767   0.997  0.32477   
## ph          -41.1184    15.2459  -2.697  0.01028 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.04 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1837, Adjusted R-squared:  0.1419 
## F-statistic: 4.389 on 2 and 39 DF,  p-value: 0.01909
anova (lm2)
## Analysis of Variance Table
## 
## Response: perc.rdw
##           Df Sum Sq Mean Sq F value  Pr(>F)  
## salinity   1  255.6  255.57  1.5040 0.22741  
## ph         1 1236.0 1236.03  7.2739 0.01028 *
## Residuals 39 6627.1  169.93                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm2)

Effect and salinity and pH on percent reproductive dry weight: daily minimum salinity

lm3 <- lm(perc.rdw ~ daily.min.sal + ph, data =all)  
lm3
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.min.sal             ph  
##      333.2979         0.1343       -40.2856
summary (lm3)
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.sal + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.3862 -10.7402   0.1131   9.1262  29.4783 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   333.2979   128.3951   2.596   0.0136 *
## daily.min.sal   0.1343     0.2765   0.486   0.6302  
## ph            -40.2856    16.1927  -2.488   0.0176 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.58 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1577, Adjusted R-squared:  0.1109 
## F-statistic: 3.369 on 2 and 36 DF,  p-value: 0.04558
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.sal  1  101.2  101.16  0.5483 0.46381  
## ph             1 1141.9 1141.94  6.1896 0.01762 *
## Residuals     36 6641.8  184.49                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect and salinity and pH on percent reproductive dry weight: daily maximum salinity

lm4 <- lm(perc.rdw ~ daily.max.sal + ph, data =all)  
lm4
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.max.sal             ph  
##      272.8572         0.7851       -34.9213
summary (lm4)
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.040 -11.154  -1.103   7.397  27.603 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   272.8572   125.7619   2.170   0.0367 *
## daily.max.sal   0.7851     0.3917   2.004   0.0526 .
## ph            -34.9213    15.6348  -2.234   0.0318 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.93 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.2373, Adjusted R-squared:  0.1949 
## F-statistic: 5.599 on 2 and 36 DF,  p-value: 0.007636
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.max.sal  1 1037.3 1037.28  6.2090 0.01745 *
## ph             1  833.4  833.43  4.9888 0.03182 *
## Residuals     36 6014.2  167.06                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect and salinity and pH on percent reproductive dry weight: daily salinity range

lm5 <- lm(perc.rdw ~ daily.sal.range + ph, data =all)  
lm5
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range + ph, data = all)
## 
## Coefficients:
##     (Intercept)  daily.sal.range               ph  
##         324.495            0.641          -39.305
summary (lm5)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -23.428 -10.336  -1.970   9.057  29.609 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)     324.4955   124.5040   2.606   0.0132 *
## daily.sal.range   0.6410     0.4422   1.449   0.1559  
## ph              -39.3047    15.7584  -2.494   0.0174 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.25 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1989, Adjusted R-squared:  0.1544 
## F-statistic: 4.469 on 2 and 36 DF,  p-value: 0.01847
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                 Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.sal.range  1  476.7  476.66  2.7166 0.10801  
## ph               1 1091.6 1091.57  6.2211 0.01735 *
## Residuals       36 6316.7  175.46                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm5)

Effect and salinity and pH on percent reproductive dry weight: daily median salinity

lm6 <- lm(perc.rdw ~ daily.med.sal + ph, data =all)  
lm6
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.sal + ph, data = all)
## 
## Coefficients:
##   (Intercept)  daily.med.sal             ph  
##      312.8853         0.3487       -38.3557
summary (lm6)
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.sal + ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.627 -11.426  -1.264   8.287  29.021 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   312.8853   127.3707   2.456   0.0190 *
## daily.med.sal   0.3487     0.2906   1.200   0.2381  
## ph            -38.3557    16.0107  -2.396   0.0219 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.36 on 36 degrees of freedom
##   (22 observations deleted due to missingness)
## Multiple R-squared:  0.1847, Adjusted R-squared:  0.1394 
## F-statistic: 4.079 on 2 and 36 DF,  p-value: 0.02532
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.rdw
##               Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.med.sal  1  431.8  431.82  2.4183 0.12868  
## ph             1 1024.8 1024.78  5.7390 0.02192 *
## Residuals     36 6428.3  178.56                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 5

lm7 <- lm(perc.rdw ~ min.daily.sal.lt5 + ph, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  min.daily.sal.lt5                 ph  
##          250.0097            -0.3383           -28.9270
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.5399  -9.7236  -0.9837   8.3821  26.1684 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       250.0097   123.4027   2.026   0.0496 *
## min.daily.sal.lt5  -0.3383     0.1527  -2.215   0.0327 *
## ph                -28.9270    15.7221  -1.840   0.0734 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.44 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2564, Adjusted R-squared:  0.2183 
## F-statistic: 6.725 on 2 and 39 DF,  p-value: 0.003095
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                   Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt5  1 1557.9 1557.93 10.0648 0.002944 **
## ph                 1  524.0  523.99  3.3852 0.073404 . 
## Residuals         39 6036.8  154.79                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 10

lm8 <- lm(perc.rdw ~ min.daily.sal.lt10 + ph, data =all)  
lm8
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt10                  ph  
##           223.5005             -0.3648            -25.3966
summary (lm8)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.8787 -10.0482  -0.0207   7.7860  25.2378 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        223.5005   127.4415   1.754   0.0873 .
## min.daily.sal.lt10  -0.3648     0.1607  -2.270   0.0288 *
## ph                 -25.3966    16.2817  -1.560   0.1269  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.41 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2606, Adjusted R-squared:  0.2227 
## F-statistic: 6.873 on 2 and 39 DF,  p-value: 0.002774
anova (lm8)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt10  1 1741.2 1741.24  11.312 0.001736 **
## ph                  1  374.5  374.50   2.433 0.126881   
## Residuals          39 6003.0  153.92                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm8)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily minimun salinity less than 15

lm9 <- lm(perc.rdw ~ min.daily.sal.lt15 +ph , data =all)  
lm9
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  min.daily.sal.lt15                  ph  
##           266.9366             -0.3319            -30.8972
summary (lm9)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -17.5783  -9.6963  -0.4866   8.4746  28.9783 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        266.9366   120.8994   2.208   0.0332 *
## min.daily.sal.lt15  -0.3319     0.1508  -2.201   0.0337 *
## ph                 -30.8972    15.4201  -2.004   0.0521 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.45 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2554, Adjusted R-squared:  0.2172 
## F-statistic:  6.69 on 2 and 39 DF,  p-value: 0.003178
anova (lm9)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## min.daily.sal.lt15  1 1451.4 1451.45  9.3643 0.003991 **
## ph                  1  622.3  622.29  4.0148 0.052085 . 
## Residuals          39 6045.0  155.00                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm9)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 5

lm10 <- lm(perc.rdw ~ max.daily.sal.lt5 +ph, data =all)  
lm10
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt5 + ph, data = all)
## 
## Coefficients:
##       (Intercept)  max.daily.sal.lt5                 ph  
##          256.3701            -0.2736           -29.8080
summary (lm10)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.5199  -9.4098   0.5204   8.3655  26.5971 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       256.3701   129.8976   1.974   0.0555 .
## max.daily.sal.lt5  -0.2736     0.1602  -1.708   0.0957 .
## ph                -29.8080    16.5681  -1.799   0.0797 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.73 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2211, Adjusted R-squared:  0.1812 
## F-statistic: 5.536 on 2 and 39 DF,  p-value: 0.007648
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                   Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt5  1 1270.5 1270.51  7.8360 0.007922 **
## ph                 1  524.8  524.81  3.2368 0.079742 . 
## Residuals         39 6323.4  162.14                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm10)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 10

lm11 <- lm(perc.rdw ~ max.daily.sal.lt10 +ph, data =all)  
lm11
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt10 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt10                  ph  
##           246.7269             -0.3037            -28.5305
summary (lm11)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt10 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.6832  -9.7477   0.5776   8.1694  26.3317 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        246.7269   128.7984   1.916   0.0628 .
## max.daily.sal.lt10  -0.3037     0.1600  -1.898   0.0651 .
## ph                 -28.5305    16.4307  -1.736   0.0904 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.63 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2337, Adjusted R-squared:  0.1944 
## F-statistic: 5.947 on 2 and 39 DF,  p-value: 0.005569
anova (lm11)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt10  1 1416.4 1416.38  8.8789 0.004945 **
## ph                  1  481.0  480.98  3.0151 0.090385 . 
## Residuals          39 6221.3  159.52                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm11)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily maximum salinity less than 15

lm12 <- lm(perc.rdw ~ max.daily.sal.lt15 +ph, data =all)  
lm12
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt15 + ph, data = all)
## 
## Coefficients:
##        (Intercept)  max.daily.sal.lt15                  ph  
##           219.8004             -0.3683            -24.9812
summary (lm12)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.sal.lt15 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.9163 -10.4790   0.4957   7.3708  25.7096 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        219.8004   127.7988   1.720   0.0934 .
## max.daily.sal.lt15  -0.3683     0.1607  -2.292   0.0274 *
## ph                 -24.9812    16.3169  -1.531   0.1338  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.39 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2623, Adjusted R-squared:  0.2245 
## F-statistic: 6.934 on 2 and 39 DF,  p-value: 0.002652
anova (lm12)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                    Df Sum Sq Mean Sq F value   Pr(>F)   
## max.daily.sal.lt15  1 1769.7 1769.66  11.524 0.001591 **
## ph                  1  360.0  359.96   2.344 0.133840   
## Residuals          39 5989.1  153.57                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm12)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily salinity range greater than 10

lm13 <- lm(perc.rdw ~ daily.sal.range.gt10 +ph, data =all)  
lm13
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt10 + ph, data = all)
## 
## Coefficients:
##          (Intercept)  daily.sal.range.gt10                    ph  
##             255.7139               -0.3193              -29.5996
summary (lm13)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt10 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.1191  -9.8037   0.2871   7.9345  25.6779 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          255.7139   126.0509   2.029   0.0494 *
## daily.sal.range.gt10  -0.3193     0.1638  -1.949   0.0585 .
## ph                   -29.5996    16.0847  -1.840   0.0734 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.6 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2372, Adjusted R-squared:  0.1981 
## F-statistic: 6.064 on 2 and 39 DF,  p-value: 0.00509
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                      Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt10  1 1388.2 1388.21  8.7425 0.005255 **
## ph                    1  537.7  537.73  3.3864 0.073354 . 
## Residuals            39 6192.8  158.79                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm13)

Effect of salinity and pH on percent reproductive dry weight: number of days with a daily salinity range greater than 5

lm14 <- lm(perc.rdw ~ daily.sal.range.gt5 +ph, data =all)  
lm14
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt5 + ph, data = all)
## 
## Coefficients:
##         (Intercept)  daily.sal.range.gt5                   ph  
##            300.6738              -0.4098             -34.9266
summary (lm14)
## 
## Call:
## lm(formula = perc.rdw ~ daily.sal.range.gt5 + ph, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -19.0547  -7.2659  -0.3372   6.3774  28.7589 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         300.6738   111.4108   2.699  0.01023 * 
## daily.sal.range.gt5  -0.4098     0.1404  -2.919  0.00581 **
## ph                  -34.9266    14.1705  -2.465  0.01822 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.96 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.313,  Adjusted R-squared:  0.2777 
## F-statistic: 8.883 on 2 and 39 DF,  p-value: 0.0006622
anova (lm14)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                     Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.sal.range.gt5  1 1672.1 1672.12 11.6915 0.001484 **
## ph                   1  868.8  868.83  6.0749 0.018221 * 
## Residuals           39 5577.8  143.02                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm14)

Different pH terms
Data for pH: ph (median pH), daily.min.ph (daily minimum pH), daily.max.ph (daily maximum pH), daily.ph.range (daily pH range), daily.med.ph (daily median pH), min.daily.ph.lt7/8 (number of days with a daily minimum pH less than 7 and 8), max.daily.ph.lt7 (number of days with a daily maximum pH less than 7), and daily.ph.range.gt0.5 (number of days with a daily pH range greater than 0.5)

Effect salinity and pH on percent reproductive dry weight: daily minimum ph

lm3 <- lm(perc.rdw ~ daily.min.ph + salinity, data =all)  
lm3
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.min.ph      salinity  
##     272.2977      -33.5408        0.2854
summary (lm3)
## 
## Call:
## lm(formula = perc.rdw ~ daily.min.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -19.681 -10.521  -3.440   7.482  31.217 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  272.2977   152.5033   1.786   0.0820 .
## daily.min.ph -33.5408    19.4738  -1.722   0.0929 .
## salinity       0.2854     0.2911   0.980   0.3330  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.69 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09994,    Adjusted R-squared:  0.05378 
## F-statistic: 2.165 on 2 and 39 DF,  p-value: 0.1283
anova (lm3)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value  Pr(>F)  
## daily.min.ph  1  631.3  631.32  3.3694 0.07405 .
## salinity      1  180.1  180.08  0.9611 0.33295  
## Residuals    39 7307.3  187.37                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm3)

Effect salinity and pH on percent reproductive dry weight: daily maximum ph

lm4 <- lm(perc.rdw ~ daily.max.ph + salinity, data =all)  
lm4
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.max.ph      salinity  
##     261.2038      -31.1587        0.2578
summary (lm4)
## 
## Call:
## lm(formula = perc.rdw ~ daily.max.ph + salinity, data = all)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20.0859 -11.8498  -0.3128   8.1814  26.9520 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  261.2038    82.7702   3.156  0.00308 **
## daily.max.ph -31.1587    10.2303  -3.046  0.00415 **
## salinity       0.2578     0.2712   0.950  0.34773   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.76 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.2176, Adjusted R-squared:  0.1775 
## F-statistic: 5.423 on 2 and 39 DF,  p-value: 0.008357
anova (lm4)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.max.ph  1 1619.4 1619.35  9.9422 0.003104 **
## salinity      1  147.1  147.14  0.9034 0.347735   
## Residuals    39 6352.2  162.88                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm4)

Effect of salinity and pH on percent reproductive dry weight: daily ph range

lm5 <- lm(perc.rdw ~ daily.ph.range + salinity, data =all)  
lm5
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range + salinity, data = all)
## 
## Coefficients:
##    (Intercept)  daily.ph.range        salinity  
##         9.2997          3.0066          0.3149
summary (lm5)
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.949 -10.018  -5.088   6.754  32.916 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      9.2997     7.1626   1.298    0.202
## daily.ph.range   3.0066     1.9065   1.577    0.123
## salinity         0.3149     0.2916   1.080    0.287
## 
## Residual standard error: 13.77 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.08954,    Adjusted R-squared:  0.04285 
## F-statistic: 1.918 on 2 and 39 DF,  p-value: 0.1605
anova (lm5)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range  1  505.8  505.85  2.6689 0.1104
## salinity        1  221.1  221.10  1.1666 0.2867
## Residuals      39 7391.7  189.53
plot (lm5)

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced

Effect salinity and pH on percent reproductive dry weight: daily median ph

lm6 <- lm(perc.rdw ~ daily.med.ph + salinity, data =all)  
lm6
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.ph + salinity, data = all)
## 
## Coefficients:
##  (Intercept)  daily.med.ph      salinity  
##     331.0367      -40.5227        0.2885
summary (lm6)
## 
## Call:
## lm(formula = perc.rdw ~ daily.med.ph + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.634 -11.044  -1.021   7.528  29.388 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  331.0367   118.5317   2.793  0.00806 **
## daily.med.ph -40.5227    14.9334  -2.714  0.00986 **
## salinity       0.2885     0.2761   1.045  0.30250   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.02 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1853, Adjusted R-squared:  0.1435 
## F-statistic: 4.435 on 2 and 39 DF,  p-value: 0.01839
anova (lm6)
## Analysis of Variance Table
## 
## Response: perc.rdw
##              Df Sum Sq Mean Sq F value   Pr(>F)   
## daily.med.ph  1 1319.2 1319.21  7.7785 0.008134 **
## salinity      1  185.2  185.17  1.0918 0.302499   
## Residuals    39 6614.3  169.60                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily minimun ph less than 7

lm7 <- lm(perc.rdw ~ min.daily.ph.lt7 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt7          salinity  
##          9.25986           0.07663           0.35603
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.423 -10.019  -5.680   8.214  33.225 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       9.25986    8.38879   1.104    0.276
## min.daily.ph.lt7  0.07663    0.46532   0.165    0.870
## salinity          0.35603    0.31929   1.115    0.272
## 
## Residual standard error: 14.19 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03215,    Adjusted R-squared:  -0.01748 
## F-statistic: 0.6478 on 2 and 39 DF,  p-value: 0.5287
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value Pr(>F)
## min.daily.ph.lt7  1   10.5  10.516  0.0522 0.8205
## salinity          1  250.5 250.517  1.2434 0.2716
## Residuals        39 7857.7 201.479
plot (lm7)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily minimun ph less than 8

lm7 <- lm(perc.rdw ~ min.daily.ph.lt8 + salinity, data =all)  
lm7
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  min.daily.ph.lt8          salinity  
##          16.6804           -0.2887            0.3295
summary (lm7)
## 
## Call:
## lm(formula = perc.rdw ~ min.daily.ph.lt8 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -18.100  -9.229  -3.563   7.970  35.480 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)  
## (Intercept)       16.6804     7.3233   2.278   0.0283 *
## min.daily.ph.lt8  -0.2887     0.1131  -2.553   0.0147 *
## salinity           0.3295     0.2780   1.185   0.2432  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.14 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1702, Adjusted R-squared:  0.1277 
## F-statistic:     4 on 2 and 39 DF,  p-value: 0.0263
anova (lm7)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value  Pr(>F)  
## min.daily.ph.lt8  1 1139.3 1139.28  6.5954 0.01417 *
## salinity          1  242.6  242.58  1.4043 0.24318  
## Residuals        39 6736.8  172.74                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily maximum ph less than 7

lm10 <- lm(perc.rdw ~ max.daily.ph.lt7 + salinity, data =all)  
lm10
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Coefficients:
##      (Intercept)  max.daily.ph.lt7          salinity  
##          9.25986           0.07663           0.35603
summary (lm10)
## 
## Call:
## lm(formula = perc.rdw ~ max.daily.ph.lt7 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.423 -10.019  -5.680   8.214  33.225 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)
## (Intercept)       9.25986    8.38879   1.104    0.276
## max.daily.ph.lt7  0.07663    0.46532   0.165    0.870
## salinity          0.35603    0.31929   1.115    0.272
## 
## Residual standard error: 14.19 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.03215,    Adjusted R-squared:  -0.01748 
## F-statistic: 0.6478 on 2 and 39 DF,  p-value: 0.5287
anova (lm10)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                  Df Sum Sq Mean Sq F value Pr(>F)
## max.daily.ph.lt7  1   10.5  10.516  0.0522 0.8205
## salinity          1  250.5 250.517  1.2434 0.2716
## Residuals        39 7857.7 201.479
plot (lm10)

Effect salinity and pH on percent reproductive dry weight: number of days with a daily ph range greater than 0.5

lm13 <- lm(perc.rdw ~ daily.ph.range.gt0.5 +salinity, data =all)  
lm13
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Coefficients:
##          (Intercept)  daily.ph.range.gt0.5              salinity  
##              15.0827               -0.4726                0.2302
summary (lm13)
## 
## Call:
## lm(formula = perc.rdw ~ daily.ph.range.gt0.5 + salinity, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.887 -12.072  -3.802   7.845  30.128 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           15.0827     8.2211   1.835   0.0742 .
## daily.ph.range.gt0.5  -0.4726     0.3600  -1.313   0.1970  
## salinity               0.2302     0.3052   0.754   0.4552  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.9 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.07246,    Adjusted R-squared:  0.02489 
## F-statistic: 1.523 on 2 and 39 DF,  p-value: 0.2307
anova (lm13)
## Analysis of Variance Table
## 
## Response: perc.rdw
##                      Df Sum Sq Mean Sq F value Pr(>F)
## daily.ph.range.gt0.5  1  478.4  478.41  2.4777 0.1235
## salinity              1  109.9  109.86  0.5690 0.4552
## Residuals            39 7530.4  193.09
plot (lm13)

####Looking at water temperature#### Effect of water temperature and salinity on density

lm6 <- lm(no.fuc.q ~ salinity + water.temp + salinity:water.temp, data =all)  
lm6
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + salinity:water.temp, 
##     data = all)
## 
## Coefficients:
##         (Intercept)             salinity           water.temp  
##            181.8952             -10.0548             -11.9875  
## salinity:water.temp  
##              0.7372
summary (lm6)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + salinity:water.temp, 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.259 -17.661  -5.154  15.469  92.223 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)         181.8952    76.6508   2.373  0.02110 * 
## salinity            -10.0548     3.1637  -3.178  0.00241 **
## water.temp          -11.9875     5.3428  -2.244  0.02882 * 
## salinity:water.temp   0.7372     0.2143   3.440  0.00111 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 27.62 on 56 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.4018, Adjusted R-squared:  0.3697 
## F-statistic: 12.54 on 3 and 56 DF,  p-value: 2.223e-06
anova (lm6)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                     Df Sum Sq Mean Sq F value    Pr(>F)    
## salinity             1   5719  5719.2  7.4966  0.008272 ** 
## water.temp           1  13944 13944.5 18.2780 7.499e-05 ***
## salinity:water.temp  1   9029  9028.8 11.8346  0.001105 ** 
## Residuals           56  42723   762.9                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm6)

Effect of water temperature, phand salinity on density

lm7 <- lm(no.fuc.q ~ salinity + water.temp +ph + salinity:water.temp:ph + salinity:ph + salinity:water.temp+ water.temp:ph, data =all)  
lm7
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + ph + salinity:water.temp:ph + 
##     salinity:ph + salinity:water.temp + water.temp:ph, data = all)
## 
## Coefficients:
##            (Intercept)                salinity              water.temp  
##                4553.12                 -245.83                 -351.33  
##                     ph             salinity:ph     salinity:water.temp  
##                -554.50                   29.93                   17.87  
##          water.temp:ph  salinity:water.temp:ph  
##                  42.94                   -2.17
summary (lm7)
## 
## Call:
## lm(formula = no.fuc.q ~ salinity + water.temp + ph + salinity:water.temp:ph + 
##     salinity:ph + salinity:water.temp + water.temp:ph, data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -50.408 -20.398   1.137  13.206  89.658 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)            4553.122   8043.785   0.566    0.575
## salinity               -245.826    414.214  -0.593    0.556
## water.temp             -351.327    510.722  -0.688    0.496
## ph                     -554.497   1022.306  -0.542    0.591
## salinity:ph              29.933     52.563   0.569    0.572
## salinity:water.temp      17.871     25.211   0.709    0.483
## water.temp:ph            42.940     64.903   0.662    0.512
## salinity:water.temp:ph   -2.170      3.199  -0.678    0.502
## 
## Residual standard error: 31.14 on 37 degrees of freedom
##   (16 observations deleted due to missingness)
## Multiple R-squared:  0.4322, Adjusted R-squared:  0.3248 
## F-statistic: 4.024 on 7 and 37 DF,  p-value: 0.002287
anova (lm7)
## Analysis of Variance Table
## 
## Response: no.fuc.q
##                        Df Sum Sq Mean Sq F value    Pr(>F)    
## salinity                1  12663 12663.2 13.0611 0.0008918 ***
## water.temp              1   5795  5794.5  5.9766 0.0193802 *  
## ph                      1    164   164.2  0.1693 0.6830988    
## salinity:ph             1      3     3.3  0.0034 0.9539401    
## salinity:water.temp     1   8233  8233.2  8.4919 0.0060212 ** 
## water.temp:ph           1      3     3.2  0.0033 0.9547002    
## salinity:water.temp:ph  1    446   446.1  0.4601 0.5017849    
## Residuals              37  35873   969.5                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot (lm7)

####Trying splines#### Try a natural spline –> need to look into what this means

library (splines)
splinefit1 <- lm (avg.oog ~ ns(salinity, knot = median (salinity)) + ns(ph, knot = median(ph)), data = all)
summary (splinefit1)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, knot = median(salinity)) + 
##     ns(ph, knot = median(ph)), data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.261 -13.201  -2.266  13.031  37.308 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                              23.15      10.26   2.256   0.0297 *
## ns(salinity, knot = median(salinity))    15.40      11.75   1.311   0.1976  
## ns(ph, knot = median(ph))               -27.20      18.61  -1.461   0.1519  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16.36 on 39 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.09727,    Adjusted R-squared:  0.05097 
## F-statistic: 2.101 on 2 and 39 DF,  p-value: 0.136
anova (splinefit1)
## Analysis of Variance Table
## 
## Response: avg.oog
##                                       Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, knot = median(salinity))  1   553.1  553.06  2.0664 0.1586
## ns(ph, knot = median(ph))              1   571.6  571.63  2.1358 0.1519
## Residuals                             39 10438.3  267.65
plot (splinefit1)

splinefit2 <- lm (avg.oog ~ ns(salinity, df = 2) + ns(ph, df =2), data = all)
summary (splinefit2)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 2) + ns(ph, df = 2), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.909 -13.709  -1.635  13.930  36.039 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             24.426     16.358   1.493    0.144
## ns(salinity, df = 2)1    7.008     20.509   0.342    0.734
## ns(salinity, df = 2)2   11.470      9.646   1.189    0.242
## ns(ph, df = 2)1        -20.315     23.659  -0.859    0.396
## ns(ph, df = 2)2        -21.082     15.239  -1.383    0.175
## 
## Residual standard error: 16.64 on 37 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1139, Adjusted R-squared:  0.0181 
## F-statistic: 1.189 on 4 and 37 DF,  p-value: 0.3318
anova (splinefit2)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 2)  2   673.3  336.64  1.2156 0.3081
## ns(ph, df = 2)        2   643.6  321.82  1.1621 0.3240
## Residuals            37 10246.0  276.92
plot (splinefit2)

splinefit3 <- lm (avg.oog ~ ns(salinity, df = 3) + ns(ph, df =3), data = all)
summary (splinefit3)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 3) + ns(ph, df = 3), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -20.875 -12.336  -3.305  14.067  34.085 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             20.085     18.568   1.082    0.287
## ns(salinity, df = 3)1    6.996     10.636   0.658    0.515
## ns(salinity, df = 3)2    1.537     27.445   0.056    0.956
## ns(salinity, df = 3)3   10.668      9.533   1.119    0.271
## ns(ph, df = 3)1        -13.127     12.557  -1.045    0.303
## ns(ph, df = 3)2         -1.991     31.625  -0.063    0.950
## ns(ph, df = 3)3        -15.021     17.203  -0.873    0.389
## 
## Residual standard error: 16.95 on 35 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:   0.13,  Adjusted R-squared:  -0.0191 
## F-statistic: 0.8719 on 6 and 35 DF,  p-value: 0.5252
anova (splinefit3)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 3)  3   785.5  261.82  0.9110 0.4456
## ns(ph, df = 3)        3   718.1  239.38  0.8329 0.4849
## Residuals            35 10059.3  287.41
plot (splinefit3)

splinefit4 <- lm (avg.oog ~ ns(salinity, df = 2) + ns(ph, df =3), data = all)
summary (splinefit4)
## 
## Call:
## lm(formula = avg.oog ~ ns(salinity, df = 2) + ns(ph, df = 3), 
##     data = all)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.486 -12.116  -3.504  13.560  34.275 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)
## (Intercept)             19.132     17.744   1.078    0.288
## ns(salinity, df = 2)1    4.852     20.791   0.233    0.817
## ns(salinity, df = 2)2   11.387      9.695   1.174    0.248
## ns(ph, df = 3)1        -13.544     12.297  -1.101    0.278
## ns(ph, df = 3)2         -1.906     31.196  -0.061    0.952
## ns(ph, df = 3)3        -15.600     16.794  -0.929    0.359
## 
## Residual standard error: 16.72 on 36 degrees of freedom
##   (19 observations deleted due to missingness)
## Multiple R-squared:  0.1291, Adjusted R-squared:  0.008154 
## F-statistic: 1.067 on 5 and 36 DF,  p-value: 0.3945
anova (splinefit4)
## Analysis of Variance Table
## 
## Response: avg.oog
##                      Df  Sum Sq Mean Sq F value Pr(>F)
## ns(salinity, df = 2)  2   673.3  336.64  1.2035 0.3119
## ns(ph, df = 3)        3   819.6  273.21  0.9767 0.4145
## Residuals            36 10070.0  279.72
plot (splinefit4)